diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8049b5b..5718fbe 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -12,13 +12,13 @@ repos: - id: pre-commit-update - repo: https://github.com/fsfe/reuse-tool - rev: v6.1.2 + rev: v6.2.0 hooks: - id: reuse - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. - rev: v0.14.1 + rev: v0.14.2 hooks: # Run the linter. - id: ruff diff --git a/docs/api_reference/architecture.md b/docs/api_reference/architecture.md index 56f76f0..09e94ba 100644 --- a/docs/api_reference/architecture.md +++ b/docs/api_reference/architecture.md @@ -6,6 +6,9 @@ SPDX-License-Identifier: MPL-2.0 # The architecture of Transformer Thermal Model +> **⚠️ Warning:** +> This overview is partly outdated and will be updated soon. + ```mermaid stateDiagram-v2 direction TB diff --git a/docs/api_reference/cooling_switch_controller.md b/docs/api_reference/cooling_switch_controller.md new file mode 100644 index 0000000..1d4a5e5 --- /dev/null +++ b/docs/api_reference/cooling_switch_controller.md @@ -0,0 +1,9 @@ + + +# Cooling switch controller + +::: transformer_thermal_model.transformer.cooling_switch_controller diff --git a/docs/examples/example_ONAN_ONAF_switch.ipynb b/docs/examples/example_ONAN_ONAF_switch.ipynb new file mode 100644 index 0000000..ba75c83 --- /dev/null +++ b/docs/examples/example_ONAN_ONAF_switch.ipynb @@ -0,0 +1,563 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e5140149", + "metadata": {}, + "source": [ + "# Switch between ONAN and ONAF\n", + "\n", + "In this example we show how to use a conditional ONAF transformer that switches between ONAN and ONAF." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a7d8f7fc", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from transformer_thermal_model.cooler import CoolerType\n", + "from transformer_thermal_model.model import Model\n", + "from transformer_thermal_model.schemas import (\n", + " InputProfile,\n", + " ThreeWindingInputProfile,\n", + " UserThreeWindingTransformerSpecifications,\n", + " UserTransformerSpecifications,\n", + " WindingSpecifications,\n", + ")\n", + "from transformer_thermal_model.schemas.thermal_model import CoolingSwitchConfig, CoolingSwitchSettings, ONANParameters\n", + "from transformer_thermal_model.transformer import PowerTransformer, ThreeWindingTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b0e1ce8e", + "metadata": {}, + "outputs": [], + "source": [ + "top_oil_label = \"Top-oil temperature\"\n", + "hot_spot_label = \"Hot-spot temperature\"\n", + "ambient_temp_label = \"Ambient temperature\"\n", + "temperature_label = \"Temperature [°C]\"\n", + "fan_switch_label = \"Fan switch\"" + ] + }, + { + "cell_type": "markdown", + "id": "1c7728d9", + "metadata": {}, + "source": [ + "We create an input profile similar to a regular simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3d44496a", + "metadata": {}, + "outputs": [], + "source": [ + "datetime_index = [pd.to_datetime(\"2025-01-01 00:00:00\") + pd.Timedelta(minutes=15 * i) for i in np.arange(0, 288)]\n", + "\n", + "load_series = pd.Series(data=np.sin(np.arange(0, 288) * 900 * 2 * np.pi * 1 / 43200) * 500 + 1200, index=datetime_index)\n", + "ambient_series = pd.Series(data=20, index=datetime_index)\n", + "\n", + "# Create an input object with the profiles\n", + "my_profile_input = InputProfile.create(\n", + " datetime_index=datetime_index, load_profile=load_series, ambient_temperature_profile=ambient_series\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b7c6e1f7", + "metadata": {}, + "source": [ + "### Create the additional CoolingSwitchSettings object\n", + "\n", + "To create a transformer with configurable fans, we create a 'normal' ONAF transformer, but with some extra input parameters. There are two ways the ONAN-ONAF switch can be configured:\n", + "\n", + " - With an historical profile\n", + " - With a threhshold temperature, at witch the fans turn on and off." + ] + }, + { + "cell_type": "markdown", + "id": "c1be3821", + "metadata": {}, + "source": [ + "#### Case 1: With an historical fan profile\n", + "\n", + "TO make a transformer with an ONAN/ONAF configuration we create the parameter class \"CoolingSwitchSettings\", this has two parameters:\n", + "- onan_specs: ONANParameters. Her we pass along all ONAN paramters that are different when the fans are turned off. \n", + "- fan_on: List[bool]. A list with booleans that indicate whether the fans are ON(ONAF) or off(ONAN)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ce2913d", + "metadata": {}, + "outputs": [], + "source": [ + "my_transformer_specifications = UserTransformerSpecifications(\n", + " load_loss=160000, # Transformer load loss [W]\n", + " nom_load_sec_side=3000, # Transformer nominal current secondary side [A]\n", + " no_load_loss=70000, # Transformer no-load loss [W]\n", + " amb_temp_surcharge=10, # Ambient temperature surcharge [K]\n", + " time_const_oil=150, # Time constant oil [min]\n", + " time_const_windings=7, # Time constant windings [min]\n", + " top_oil_temp_rise=50.5, # Top-oil temperature rise [K]\n", + " winding_oil_gradient=23, # Winding oil gradient (worst case) [K]\n", + " end_temp_reduction=0, # Lowering of the end temperature with respect to the current specification [K]\n", + " hot_spot_fac=1.2, # Hot-spot factor [-]\n", + " oil_const_k11=0.5, # Oil constant k11 [-]\n", + " winding_const_k21=2.0, # Winding constant k21 [-]\n", + " winding_const_k22=2.0, # Winding constant k22 [-]\n", + " oil_exp_x=0.8, # Oil exponent x [-]\n", + " winding_exp_y=1.3, # Winding exponent x [-]\n", + ")\n", + "\n", + "onan_specs = ONANParameters(\n", + " top_oil_temp_rise=50.5,\n", + " time_const_oil=150,\n", + " time_const_windings=7,\n", + " load_loss=160000,\n", + " nom_load_sec_side=1600, # Lower nominal current for ONAN mode\n", + " winding_oil_gradient=23,\n", + " hot_spot_fac=1.2,\n", + ")\n", + "\n", + "# Create a fan schedule where the fans are off for the first half of the time and on for the second half\n", + "is_on = [False] * len(datetime_index)\n", + "for i in range(len(is_on) // 2, len(is_on)):\n", + " is_on[i] = True\n", + "\n", + "onaf_switch = CoolingSwitchSettings(fan_on=is_on, onan_parameters=onan_specs)" + ] + }, + { + "cell_type": "markdown", + "id": "1cfa984a", + "metadata": {}, + "source": [ + "We then define a PowerTransformer with the additional parameter `onaf_switch`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0a5d06e2", + "metadata": {}, + "outputs": [], + "source": [ + "my_transformer = PowerTransformer(\n", + " user_specs=my_transformer_specifications, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch\n", + ")\n", + "my_model = Model(temperature_profile=my_profile_input, transformer=my_transformer)\n", + "results = my_model.run()" + ] + }, + { + "cell_type": "markdown", + "id": "a132ad56", + "metadata": {}, + "source": [ + "You can clearly see that both the hotspot and topoil temperature drop when the fans turn on:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7082e5c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "start_time = datetime_index[0] + pd.Timedelta(my_transformer.specs.time_const_oil * 5, \"m\")\n", + "fig = plt.figure()\n", + "ax = results.top_oil_temp_profile.loc[start_time::].plot(label=top_oil_label, color=\"green\")\n", + "results.hot_spot_temp_profile.loc[start_time::].plot(label=hot_spot_label, color=\"blue\")\n", + "ambient_series.loc[start_time::].plot(label=ambient_temp_label, color=\"red\")\n", + "ax.set_ylabel(temperature_label)\n", + "ax.hlines(120, datetime_index[0], datetime_index[-1], linestyles=\"dashed\", label=\"Hot-spot limit\", color=\"blue\")\n", + "ax.hlines(105, datetime_index[0], datetime_index[-1], linestyles=\"dashed\", label=\"Top-oil limit\", color=\"green\")\n", + "ax.legend(loc=\"lower left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3e48c852", + "metadata": {}, + "source": [ + "#### Case 2: With a threshold temperature\n", + "\n", + "To use a temperature threshold to turn on and off the fans we use the same `CoolingSwitchSettings` class, but instead off a fan_on list, \n", + "we provide a temperature_threshold, which should be contructed with the `CoolingSwitchConfig` class:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1b70cb8", + "metadata": {}, + "outputs": [], + "source": [ + "onaf_switch = CoolingSwitchSettings(\n", + " temperature_threshold=CoolingSwitchConfig(activation_temp=75, deactivation_temp=65),\n", + " onan_parameters=onan_specs,\n", + ")\n", + "my_transformer = PowerTransformer(\n", + " user_specs=my_transformer_specifications, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch\n", + ")\n", + "my_model = Model(temperature_profile=my_profile_input, transformer=my_transformer)\n", + "results = my_model.run()" + ] + }, + { + "cell_type": "markdown", + "id": "4868198d", + "metadata": {}, + "source": [ + "In the plots we can see that, as soon as the top-oil temperature reaches 75 degrees, the fans turn on and the temperature drops. Note that at a top-oil temperature of 65, the fans turn off again, and the temperature rises again." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4007c97a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "start_time = datetime_index[0] + pd.Timedelta(my_transformer.specs.time_const_oil * 5, \"m\")\n", + "fig = plt.figure()\n", + "ax = results.top_oil_temp_profile.loc[start_time::].plot(label=top_oil_label, color=\"green\")\n", + "results.hot_spot_temp_profile.loc[start_time::].plot(label=\"Hot-spot temperature\", color=\"blue\")\n", + "ambient_series.loc[start_time::].plot(label=\"Ambient temperature\", color=\"red\")\n", + "ax.set_ylabel(\"Temperature [C]\")\n", + "ax.hlines(120, datetime_index[0], datetime_index[-1], linestyles=\"dashed\", label=\"Hot-spot limit\", color=\"blue\")\n", + "ax.hlines(105, datetime_index[0], datetime_index[-1], linestyles=\"dashed\", label=\"Top-oil limit\", color=\"green\")\n", + "ax.legend(loc=\"lower left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fd07416b", + "metadata": {}, + "source": [ + "### ONAN ONAF for a three winding transformer\n", + "\n", + "For three winding transformers, the principle is the same, but since the onan parameters are different, we have to use different schemas." + ] + }, + { + "cell_type": "markdown", + "id": "44f4772c", + "metadata": {}, + "source": [ + "Create the load and the specs the same way as for a normal ONAF transformer:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1252294f", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the time range for your simulation\n", + "datetime_index = [pd.to_datetime(\"2025-07-01 00:00:00\") + pd.Timedelta(minutes=15 * i) for i in np.arange(0, 288)]\n", + "\n", + "load_series_high = pd.Series(\n", + " data=np.sin(np.arange(0, 288) * 900 * 2 * np.pi * 1 / 43200) * 500 + 500, index=datetime_index\n", + ")\n", + "load_series_middle = pd.Series(\n", + " data=np.sin(np.arange(0, 288) * 900 * 2 * np.pi * 1 / 43200) * 500 + 500, index=datetime_index\n", + ")\n", + "load_series_low = pd.Series(\n", + " data=np.sin(np.arange(0, 288) * 900 * 2 * np.pi * 1 / 43200) * 500 + 500, index=datetime_index\n", + ")\n", + "\n", + "ambient_series = pd.Series(data=20, index=datetime_index)\n", + "\n", + "# Create the input profile for the three-winding transformer\n", + "profile_input = ThreeWindingInputProfile.create(\n", + " datetime_index=datetime_index,\n", + " ambient_temperature_profile=ambient_series,\n", + " load_profile_high_voltage_side=load_series_high,\n", + " load_profile_middle_voltage_side=load_series_middle,\n", + " load_profile_low_voltage_side=load_series_low,\n", + ")\n", + "\n", + "# Define the transformer specifications for each winding\n", + "user_specs = UserThreeWindingTransformerSpecifications(\n", + " no_load_loss=20,\n", + " amb_temp_surcharge=10,\n", + " lv_winding=WindingSpecifications(\n", + " nom_load=1000, winding_oil_gradient=20, hot_spot_fac=1.2, time_const_winding=1, nom_power=1000\n", + " ),\n", + " mv_winding=WindingSpecifications(\n", + " nom_load=1000, winding_oil_gradient=20, hot_spot_fac=1.2, time_const_winding=1, nom_power=1000\n", + " ),\n", + " hv_winding=WindingSpecifications(\n", + " nom_load=2000, winding_oil_gradient=20, hot_spot_fac=1.2, time_const_winding=1, nom_power=2000\n", + " ),\n", + " load_loss_hv_lv=100,\n", + " load_loss_hv_mv=100,\n", + " load_loss_mv_lv=100,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "151f0147", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2331e96f", + "metadata": {}, + "outputs": [], + "source": [ + "from transformer_thermal_model.schemas.thermal_model.onaf_switch import (\n", + " ThreeWindingCoolingSwitchSettings,\n", + " ThreeWindingONANParameters,\n", + ")\n", + "\n", + "onan_parameters = ThreeWindingONANParameters(\n", + " lv_winding=WindingSpecifications(\n", + " time_const_winding=1, nom_load=900, winding_oil_gradient=20, hot_spot_fac=1.2, nom_power=1000\n", + " ),\n", + " mv_winding=WindingSpecifications(\n", + " time_const_winding=1, nom_load=900, winding_oil_gradient=20, hot_spot_fac=1.2, nom_power=1000\n", + " ),\n", + " hv_winding=WindingSpecifications(\n", + " time_const_winding=1, nom_load=1800, winding_oil_gradient=20, hot_spot_fac=1.2, nom_power=2000\n", + " ),\n", + " top_oil_temp_rise=60,\n", + " time_const_oil=150,\n", + " load_loss_mv_lv=100,\n", + " load_loss_hv_lv=100,\n", + " load_loss_hv_mv=100,\n", + ")\n", + "\n", + "# split in three parts: off, on, off\n", + "point_1 = 70\n", + "point_2 = 70\n", + "is_on = [False] * point_1 + [True] * (len(profile_input.datetime_index) - point_1 - point_2) + [False] * point_2\n", + "\n", + "onaf_switch = ThreeWindingCoolingSwitchSettings(\n", + " fan_on=is_on, temperature_threshold=None, onan_parameters=onan_parameters\n", + ")\n", + "transformer = ThreeWindingTransformer(\n", + " user_specs=user_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch\n", + ")\n", + "model = Model(transformer=transformer, temperature_profile=profile_input)\n", + "results = model.run()\n", + "\n", + "full_onaf_transformer = ThreeWindingTransformer(user_specs=user_specs, cooling_type=CoolerType.ONAF)\n", + "full_onaf_model = Model(transformer=full_onaf_transformer, temperature_profile=profile_input)\n", + "full_onaf_results = full_onaf_model.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbaf6bfe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the top-oil temperature profile\n", + "fig1, ax1 = plt.subplots()\n", + "results.top_oil_temp_profile.plot(ax=ax1, label=top_oil_label)\n", + "full_onaf_results.top_oil_temp_profile.plot(ax=ax1, label=\"Full ONAF top-oil temperature\", linestyle=\"dashed\")\n", + "ax1.axvline(profile_input.datetime_index[point_1], color=\"black\", linestyle=\"dotted\", label=fan_switch_label)\n", + "ax1.axvline(\n", + " profile_input.datetime_index[len(profile_input.datetime_index) - point_2],\n", + " color=\"black\",\n", + " linestyle=\"dotted\",\n", + " label=fan_switch_label,\n", + ")\n", + "ax1.set_title(top_oil_label)\n", + "ax1.set_ylabel(temperature_label)\n", + "ax1.legend()\n", + "plt.show()\n", + "\n", + "# Plot the hot-spot temperature profiles\n", + "fig2, ax2 = plt.subplots()\n", + "results.hot_spot_temp_profile.plot(ax=ax2)\n", + "ax2.axvline(profile_input.datetime_index[point_1], color=\"black\", linestyle=\"dotted\", label=fan_switch_label)\n", + "ax2.axvline(\n", + " profile_input.datetime_index[len(profile_input.datetime_index) - point_2],\n", + " color=\"black\",\n", + " linestyle=\"dotted\",\n", + " label=fan_switch_label,\n", + ")\n", + "ax2.set_title(\"Hot-spot Temperatures\")\n", + "ax2.set_ylabel(temperature_label)\n", + "ax2.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "118821a1", + "metadata": {}, + "source": [ + "#### Three winding transformers with an ONAN/ONAF switch that uses threshold temperature:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c3c1eb3", + "metadata": {}, + "outputs": [], + "source": [ + "# Create ONAF switch configuration with temperature thresholds\n", + "# Fans activate when top-oil temperature reaches 60°C, deactivate at 50°C\n", + "onaf_switch = ThreeWindingCoolingSwitchSettings(\n", + " temperature_threshold=CoolingSwitchConfig(activation_temp=60, deactivation_temp=50), onan_parameters=onan_parameters\n", + ")\n", + "\n", + "# Create transformer with automatic ONAN/ONAF switching capability\n", + "# Starts in ONAF mode but will switch to ONAN if temperature drops below threshold\n", + "transformer = ThreeWindingTransformer(\n", + " user_specs=user_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch\n", + ")\n", + "\n", + "# Run thermal model with automatic cooling mode switching\n", + "model = Model(transformer=transformer, temperature_profile=profile_input)\n", + "results = model.run()\n", + "\n", + "# Create comparison transformer that stays in ONAF mode throughout the simulation\n", + "# This shows the difference between automatic switching and constant ONAF operation\n", + "full_onaf_transformer = ThreeWindingTransformer(user_specs=user_specs, cooling_type=CoolerType.ONAF)\n", + "full_onaf_model = Model(transformer=full_onaf_transformer, temperature_profile=profile_input)\n", + "full_onaf_results = full_onaf_model.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e7edc0e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot comparison of top-oil temperatures: automatic switching vs. constant ONAF\n", + "fig1, ax1 = plt.subplots()\n", + "# Transformer with automatic ONAN/ONAF switching based on temperature thresholds\n", + "results.top_oil_temp_profile.plot(ax=ax1, label=top_oil_label)\n", + "# Reference transformer that stays in ONAF mode throughout simulation\n", + "full_onaf_results.top_oil_temp_profile.plot(ax=ax1, label=\"Full ONAF top-oil temperature\", linestyle=\"dashed\")\n", + "ax1.set_title(top_oil_label)\n", + "ax1.set_ylabel(temperature_label)\n", + "ax1.legend()\n", + "plt.show()\n", + "\n", + "# Plot hot-spot temperature profiles for all three windings\n", + "# Shows how each winding responds to the automatic cooling mode switching\n", + "fig2, ax2 = plt.subplots()\n", + "results.hot_spot_temp_profile.plot(ax=ax2)\n", + "ax2.set_title(hot_spot_label)\n", + "ax2.set_ylabel(temperature_label)\n", + "ax2.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "transformer-thermal-model-XmhHfE8S-py3.12", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/examples/example_ONAN_ONAF_switch.ipynb.license b/docs/examples/example_ONAN_ONAF_switch.ipynb.license new file mode 100644 index 0000000..593919f --- /dev/null +++ b/docs/examples/example_ONAN_ONAF_switch.ipynb.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: Contributors to the Transformer Thermal Model project + +SPDX-License-Identifier: MPL-2.0 diff --git a/docs/examples/power_transformer_example.ipynb b/docs/examples/power_transformer_example.ipynb index eb39419..9a9cc57 100644 --- a/docs/examples/power_transformer_example.ipynb +++ b/docs/examples/power_transformer_example.ipynb @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -222,7 +222,7 @@ "ax.set_ylabel(\"Temperature [C]\")\n", "ax.hlines(120, datetime_index[0], datetime_index[-1], linestyles=\"dashed\", label=\"Hot-spot limit\", color=\"blue\")\n", "ax.hlines(105, datetime_index[0], datetime_index[-1], linestyles=\"dashed\", label=\"Top-oil limit\", color=\"green\")\n", - "ax.legend(loc=\"lower left\");" + "ax.legend(loc=\"lower left\")" ] }, { @@ -292,7 +292,7 @@ "ax.set_title(\n", " f\"Aging profile with total aging of {round(total_aging, 2)} days over a\"\n", " \" total of {len(set(aging_profile.index.date))} days.\"\n", - ");" + ")" ] } ], diff --git a/docs/get_started/about.md b/docs/get_started/about.md index 1eb84b7..57e7d63 100644 --- a/docs/get_started/about.md +++ b/docs/get_started/about.md @@ -491,6 +491,250 @@ hot_spot_temp_profile = results.hot_spot_temp_profile Note, how the top oil temperature we receive as the output `results.top_oil_temp_profile` exactly matches the top oil temperature we provided as the input. +### Model a transformer that switches between ONAN and ONAF + +Some transformers operate with forced cooling (ONAF) only when needed. When the cooling fans are OFF, the transformer +is in ONAN mode. When the fans are ON, the transformer is in ONAF mode. The library supports modelling a transformer +that dynamically switches between these modes using the `CoolingSwitchController` and an `CoolingSwitchSettings` configuration +object. + +You can configure switching in two mutually exclusive ways: + +1. **Fan status schedule** (historical or planned operation): provide a list of booleans (`fan_on`) indicating + for each time step whether the fans are ON (`True`) or OFF (`False`). +2. **Temperature threshold control**: provide activation and deactivation temperatures. The fans turn ON when the + top‑oil temperature reaches the activation temperature and turn OFF when it falls below the deactivation temperature. + +In both cases, you must also supply a set of ONAN parameters (`onan_parameters`) describing the specification values +that differ when the fans are OFF (such as nominal load, winding oil gradient, hot‑spot factor, losses, and time +constants). + +The `CoolingSwitchController` keeps a deep copy of the original ONAF specifications and, whenever a switch event +occurs, applies either the ONAN parameters or restores the original ONAF specifications. Switching is evaluated at +each time step during the thermal model run, so temperature profiles reflect the current cooling mode immediately. + +#### 1. Switching using a fan status schedule + +```python +import pandas as pd + +from transformer_thermal_model.cooler import CoolerType +from transformer_thermal_model.model import Model +from transformer_thermal_model.schemas import InputProfile, UserTransformerSpecifications +from transformer_thermal_model.schemas.thermal_model import CoolingSwitchSettings, ONANParameters +from transformer_thermal_model.transformer import PowerTransformer + +# User (ONAF) specifications +user_specs = UserTransformerSpecifications( + load_loss=1000, + nom_load_sec_side=1500, + no_load_loss=200, + amb_temp_surcharge=20, + time_const_windings=10, +) + +# ONAN specifications used when fans are OFF (values that differ from ONAF mode) +onan_params = ONANParameters( + top_oil_temp_rise=65, # Different top‑oil rise + time_const_oil=8, # Different oil time constant + time_const_windings=10, + load_loss=1000, + nom_load_sec_side=1200, # Lower nominal current in ONAN mode + winding_oil_gradient=20, + hot_spot_fac=1.3, +) + +# Create a schedule: first half OFF (ONAN), second half ON (ONAF) +fan_on = [False]*2*24*7 + [True]*2*24*7 # Example for a week with 15-min intervals + +onaf_switch = CoolingSwitchSettings( + fan_on=fan_on, + onan_parameters=onan_params, +) + +transformer = PowerTransformer( + user_specs=user_specs, + cooling_type=CoolerType.ONAF, # Overall transformer supports ONAF + cooling_switch_settings=onaf_switch, +) +one_week = 4*24*7 +datetime_index = pd.date_range("2020-01-01", periods=one_week, freq="15min") + +nominal_load = 100 +load_points = pd.Series([nominal_load] * one_week, index=datetime_index) +ambient_temp = 21 +temperature_points = pd.Series([ambient_temp] * one_week, index=datetime_index) + +profile_input = InputProfile.create( + datetime_index = datetime_index, + load_profile = load_points, + ambient_temperature_profile = temperature_points, +) + +model = Model(transformer=transformer, temperature_profile=profile_input) +results = model.run() +``` + +During the run the model applies ONAN parameters for the initial OFF section, +then switches to the original ONAF specs when `fan_on` becomes `True`. + +##### 2. Switching using temperature thresholds + +```python +import pandas as pd + +from transformer_thermal_model.cooler import CoolerType +from transformer_thermal_model.model import Model +from transformer_thermal_model.schemas import InputProfile, UserTransformerSpecifications +from transformer_thermal_model.schemas.thermal_model import CoolingSwitchConfig, CoolingSwitchSettings, ONANParameters +from transformer_thermal_model.transformer import PowerTransformer + +# User (ONAF) specifications +user_specs = UserTransformerSpecifications( + load_loss=1000, + nom_load_sec_side=1500, + no_load_loss=200, + amb_temp_surcharge=20, + time_const_windings=10, +) + +onan_params = ONANParameters( + top_oil_temp_rise=65, + time_const_oil=8, + time_const_windings=10, + load_loss=1000, + nom_load_sec_side=1200, + winding_oil_gradient=20, + hot_spot_fac=1.3, +) + +# Fans turn ON at 70 °C and OFF again at 60 °C +threshold = CoolingSwitchConfig(activation_temp=70, deactivation_temp=60) + +onaf_switch = CoolingSwitchSettings( + temperature_threshold=threshold, + onan_parameters=onan_params, +) + +transformer = PowerTransformer( + user_specs=user_specs, + cooling_type=CoolerType.ONAF + , + cooling_switch_settings=onaf_switch, +) +one_week = 4*24*7 +datetime_index = pd.date_range("2020-01-01", periods=one_week, freq="15min") + +nominal_load = 100 +load_points = pd.Series([nominal_load] * one_week, index=datetime_index) +ambient_temp = 21 +temperature_points = pd.Series([ambient_temp] * one_week, index=datetime_index) + + +profile_input = InputProfile.create( + datetime_index = datetime_index, + load_profile = load_points, + ambient_temperature_profile = temperature_points, +) + +results = Model(transformer=transformer, temperature_profile=profile_input).run() +``` + +Each time step the controller compares the previous and current top‑oil temperature to the +activation / deactivation thresholds and switches mode accordingly. + +#### Three‑winding transformers + +For three‑winding transformers a dedicated `ThreeWindingCoolingSwitchSettings` and `ThreeWindingONANParameters` +are available, allowing you to specify ONAN parameters per winding (LV, MV, HV) and the separate load losses. +Usage is analogous: + +```python +import pandas as pd + +from transformer_thermal_model.cooler import CoolerType +from transformer_thermal_model.model import Model +from transformer_thermal_model.schemas import ( + ThreeWindingInputProfile, + UserThreeWindingTransformerSpecifications, + WindingSpecifications, +) +from transformer_thermal_model.schemas.thermal_model.onaf_switch import ( + ThreeWindingCoolingSwitchSettings, + ThreeWindingONANParameters, +) +from transformer_thermal_model.transformer import ThreeWindingTransformer + +onan_parameters = ThreeWindingONANParameters( + lv_winding=WindingSpecifications( + nom_load=900, winding_oil_gradient=20, hot_spot_fac=1.2, + time_const_winding=1, nom_power=1000), + mv_winding=WindingSpecifications( + nom_load=900, winding_oil_gradient=20, hot_spot_fac=1.2, + time_const_winding=1, nom_power=1000), + hv_winding=WindingSpecifications( + nom_load=1800, winding_oil_gradient=20, hot_spot_fac=1.2, + time_const_winding=1, nom_power=2000), + top_oil_temp_rise=60, + time_const_oil=150, + load_loss_mv_lv=100, + load_loss_hv_lv=100, + load_loss_hv_mv=100, +) + +switch_cfg = ThreeWindingCoolingSwitchSettings( + fan_on=[False]*144 + [True]*144, + onan_parameters=onan_parameters, +) + +user_specs = UserThreeWindingTransformerSpecifications( + no_load_loss=20, + amb_temp_surcharge=10, + lv_winding=WindingSpecifications( + nom_load=1000, winding_oil_gradient=20, hot_spot_fac=1.2, + time_const_winding=1, nom_power=1000), + mv_winding=WindingSpecifications( + nom_load=1000, winding_oil_gradient=20, hot_spot_fac=1.2, + time_const_winding=1, nom_power=1000), + hv_winding=WindingSpecifications( + nom_load=2000, winding_oil_gradient=20, hot_spot_fac=1.2, + time_const_winding=1, nom_power=2000), + load_loss_hv_lv=100, + load_loss_hv_mv=100, + load_loss_mv_lv=100, +) + +transformer = ThreeWindingTransformer( + user_specs=user_specs, + cooling_type=CoolerType.ONAF, + cooling_switch_settings=switch_cfg, +) + +# Create the input profile for the three-winding transformer +datetime_index = [pd.to_datetime("2025-07-01 00:00:00") + pd.Timedelta(minutes=15 * i) for i in range(0, 288)] +profile_input = ThreeWindingInputProfile.create( + datetime_index=datetime_index, + ambient_temperature_profile=pd.Series(data=900, index=datetime_index), + load_profile_high_voltage_side=pd.Series(data=500, index=datetime_index), + load_profile_middle_voltage_side=pd.Series(data=500, index=datetime_index), + load_profile_low_voltage_side=pd.Series(data=300, index=datetime_index), +) + +results = Model(transformer=transformer, temperature_profile=profile_input).run() +``` + +#### When should you use switching? + +Use dynamic switching when: + +- You want more realistic temperature profiles under partial cooling operation. +- You need to assess thermal limits or aging for both fan operating states. +- You are performing what‑if analyses on fan activation strategy. + +If your transformer always runs with fans engaged (forced cooling), simply use `CoolerType.ONAF` without a switch configuration. + +See the example notebook `examples/example_ONAN_ONAF_switch.ipynb` for a full demonstration with plots. + ## License This project is licensed under the Mozilla Public License, version 2.0 - see diff --git a/mkdocs.yml b/mkdocs.yml index 2590162..0b82d81 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -57,6 +57,7 @@ nav: - Aging: api_reference/aging.md - Components: api_reference/components.md - Cooler: api_reference/cooler.md + - ONAN/ONAF Switch: api_reference/cooling_switch_controller.md - Toolbox: api_reference/toolbox.md - Examples: - Quick start: examples/quickstart.ipynb @@ -65,6 +66,7 @@ nav: - A three winding transformer: examples/three-winding-calculation.ipynb - Hot-spot factor calibration: examples/hot-spot_calibration.ipynb - Modelling with an initial state: examples/example_initial_state.ipynb + - Switch between ONAN and ONAF state: examples/example_ONAN_ONAF_switch.ipynb - Theoretical Documentation: - Overview: theoretical_documentation/overview.md - Transformer definition: theoretical_documentation/transformer_definition.md diff --git a/poetry.lock b/poetry.lock index 54f910c..52717e8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. 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constant_load_profile(): + """Create a constant load profile for testing.""" + data_points = 4 * 24 * 7 + datetime_index = pd.date_range("2021-01-01 00:00:00", periods=data_points, freq="h") + load_profile = [1000] * data_points + ambient_temperature_profile = [30] * data_points + return InputProfile.create( + datetime_index=datetime_index, + ambient_temperature_profile=ambient_temperature_profile, + load_profile=load_profile, + ) + + +@pytest.fixture(scope="function") +def constant_load_profile_minutes(): + """Create a constant load profile for testing.""" + data_points = 4 * 60 * 24 * 7 + datetime_index = pd.date_range("2021-01-01 00:00:00", periods=data_points, freq="min") + load_profile = [1000] * data_points + ambient_temperature_profile = [30] * data_points + return InputProfile.create( + datetime_index=datetime_index, + ambient_temperature_profile=ambient_temperature_profile, + load_profile=load_profile, + ) diff --git a/tests/model/test_onan_onaf_switch.py b/tests/model/test_onan_onaf_switch.py new file mode 100644 index 0000000..4d39327 --- /dev/null +++ b/tests/model/test_onan_onaf_switch.py @@ -0,0 +1,469 @@ +# SPDX-FileCopyrightText: Contributors to the Transformer Thermal Model project +# +# SPDX-License-Identifier: MPL-2.0 + +import math + +import numpy as np +import pytest + +from transformer_thermal_model.cooler import CoolerType +from transformer_thermal_model.model.thermal_model import Model +from transformer_thermal_model.schemas.specifications.transformer import ( + UserThreeWindingTransformerSpecifications, + UserTransformerSpecifications, +) +from transformer_thermal_model.schemas.thermal_model.input_profile import ThreeWindingInputProfile +from transformer_thermal_model.schemas.thermal_model.onaf_switch import ( + CoolingSwitchConfig, + CoolingSwitchSettings, + ONANParameters, + ThreeWindingCoolingSwitchSettings, + ThreeWindingONANParameters, + WindingSpecifications, +) +from transformer_thermal_model.transformer.power import PowerTransformer +from transformer_thermal_model.transformer.threewinding import ThreeWindingTransformer + + +def test_start_cooling_type(default_user_trafo_specs: UserTransformerSpecifications): + """Check that the transformer starts with the correct cooling type.""" + default_user_trafo_specs.top_oil_temp_rise = 60 + default_user_trafo_specs.winding_oil_gradient = 25 + default_user_trafo_specs.hot_spot_fac = 1.1 + + is_on = [True] * 100 + + onan_parameters = ONANParameters( + top_oil_temp_rise=50.5, + time_const_oil=150, + time_const_windings=7, + load_loss=default_user_trafo_specs.load_loss, + nom_load_sec_side=1600, + winding_oil_gradient=23, + hot_spot_fac=1.2, + ) + + onaf_switch = CoolingSwitchSettings( + fan_on=is_on, + temperature_threshold=None, + onan_parameters=onan_parameters, + ) + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + transformer.set_ONAN_ONAF_first_timestamp(init_top_oil_temp=20) + assert transformer.specs.nom_load_sec_side == default_user_trafo_specs.nom_load_sec_side + assert transformer.specs.top_oil_temp_rise == default_user_trafo_specs.top_oil_temp_rise + assert transformer.specs.winding_oil_gradient == default_user_trafo_specs.winding_oil_gradient + assert transformer.specs.hot_spot_fac == default_user_trafo_specs.hot_spot_fac + + is_on = [False] * 100 + onaf_switch.fan_on = is_on + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + transformer.set_ONAN_ONAF_first_timestamp(init_top_oil_temp=20) + assert transformer.specs.nom_load_sec_side == onaf_switch.onan_parameters.nom_load_sec_side + assert transformer.specs.top_oil_temp_rise == onaf_switch.onan_parameters.top_oil_temp_rise + assert transformer.specs.winding_oil_gradient == onaf_switch.onan_parameters.winding_oil_gradient + assert transformer.specs.hot_spot_fac == onaf_switch.onan_parameters.hot_spot_fac + + onaf_switch = CoolingSwitchSettings( + fan_on=None, + temperature_threshold=CoolingSwitchConfig(activation_temp=85, deactivation_temp=75), + onan_parameters=onan_parameters, + ) + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + transformer.set_ONAN_ONAF_first_timestamp(init_top_oil_temp=20) + assert transformer.specs.nom_load_sec_side == onaf_switch.onan_parameters.nom_load_sec_side + assert transformer.specs.top_oil_temp_rise == onaf_switch.onan_parameters.top_oil_temp_rise + assert transformer.specs.winding_oil_gradient == onaf_switch.onan_parameters.winding_oil_gradient + assert transformer.specs.hot_spot_fac == onaf_switch.onan_parameters.hot_spot_fac + + # If the initial top-oil temperature is above the activation temperature, it should start in ONAF mode + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + transformer.set_ONAN_ONAF_first_timestamp(init_top_oil_temp=90) + assert transformer.specs.nom_load_sec_side == default_user_trafo_specs.nom_load_sec_side + assert transformer.specs.top_oil_temp_rise == default_user_trafo_specs.top_oil_temp_rise + assert transformer.specs.winding_oil_gradient == default_user_trafo_specs.winding_oil_gradient + assert transformer.specs.hot_spot_fac == default_user_trafo_specs.hot_spot_fac + + +def test_wrong_onaf_switch(default_user_trafo_specs: UserTransformerSpecifications, iec_load_profile): + """Check that a ValueError is raised when the length of fan_on does not match the temperature profile.""" + is_on = [True] * 100 + onan_parameters = ONANParameters( + top_oil_temp_rise=50.5, + time_const_oil=150, + time_const_windings=7, + load_loss=default_user_trafo_specs.load_loss, + nom_load_sec_side=1600, + winding_oil_gradient=23, + hot_spot_fac=1.2, + ) + onaf_switch = CoolingSwitchSettings(fan_on=is_on, temperature_threshold=None, onan_parameters=onan_parameters) + + with pytest.raises(ValueError, match=("ONAF switch only works when the cooling type is ONAF.")): + PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAN, cooling_switch_settings=onaf_switch + ) + + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + with pytest.raises( + ValueError, + match=( + "The length of the fan_on list in the cooling_switch_settings must be equal to the length of " + "the temperature profile." + ), + ): + Model(transformer=transformer, temperature_profile=iec_load_profile) + + with pytest.raises( + ValueError, + match=("Activation temperature must be higher than deactivation temperature."), + ): + CoolingSwitchSettings( + fan_on=None, + temperature_threshold=CoolingSwitchConfig(activation_temp=50, deactivation_temp=60), + onan_parameters=onan_parameters, + ) + + # Provide either 'fan_on' or 'temperature_threshold', not both. + with pytest.raises(ValueError, match=("Provide either 'fan_on' or 'temperature_threshold', not both")): + CoolingSwitchSettings( + temperature_threshold=CoolingSwitchConfig(activation_temp=80, deactivation_temp=70), + onan_parameters=onan_parameters, + fan_on=[True, False], + ) + with pytest.raises(ValueError, match=("Either 'fan_on' or 'temperature_threshold' must be provided.")): + CoolingSwitchSettings(temperature_threshold=None, onan_parameters=onan_parameters, fan_on=None) + + +def test_complete_onan_onaf_switch_fan_on( + default_user_trafo_specs: UserTransformerSpecifications, constant_load_profile +): + """Check that the transformer can handle a complete ONAF switch scenario.""" + default_user_trafo_specs.top_oil_temp_rise = 60 + default_user_trafo_specs.winding_oil_gradient = 25 + default_user_trafo_specs.hot_spot_fac = 1.1 + default_user_trafo_specs.nom_load_sec_side = constant_load_profile.load_profile[0] * 1.2 + + is_on = [False] * 50 + [True] * (len(constant_load_profile.datetime_index) - 50) + onan_parameters = ONANParameters( + top_oil_temp_rise=50.5, + time_const_oil=150, + time_const_windings=7, + load_loss=default_user_trafo_specs.load_loss, + nom_load_sec_side=constant_load_profile.load_profile[0] * 0.8, + winding_oil_gradient=23, + hot_spot_fac=1.2, + ) + onaf_switch = CoolingSwitchSettings(fan_on=is_on, temperature_threshold=None, onan_parameters=onan_parameters) + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + model = Model(transformer=transformer, temperature_profile=constant_load_profile) + output = model.run() + + # After 50 steps, the cooling should switch to ONAF and the top-oil temperature should be lower + assert output.top_oil_temp_profile.iloc[45] > output.top_oil_temp_profile.iloc[55] + + # Test that it correctly switches back to ONAN if the fans are turned off again + is_on = [False] * 50 + [True] * 30 + [False] * (len(constant_load_profile.datetime_index) - 80) + onaf_switch.fan_on = is_on + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + model = Model(transformer=transformer, temperature_profile=constant_load_profile) + output_2 = model.run() + assert output_2.top_oil_temp_profile.iloc[45] > output_2.top_oil_temp_profile.iloc[55] + assert output_2.top_oil_temp_profile.iloc[75] < output_2.top_oil_temp_profile.iloc[85] + + # Check that an onan onaf switch with long periods of onaf reaches the same steady state as a constant onaf + onaf_transformer = PowerTransformer(user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF) + onaf_model = Model(transformer=onaf_transformer, temperature_profile=constant_load_profile) + onaf_output = onaf_model.run() + assert math.isclose(onaf_output.top_oil_temp_profile.iloc[-1], output.top_oil_temp_profile.iloc[-1], rel_tol=1e-2) + assert math.isclose(onaf_output.hot_spot_temp_profile.iloc[-1], output.hot_spot_temp_profile.iloc[-1], rel_tol=1e-2) + + +def test_complete_onan_onaf_switch_temp_threshold( + default_user_trafo_specs: UserTransformerSpecifications, constant_load_profile_minutes +): + """Check that the transformer can handle a complete ONAF switch scenario based on temperature thresholds.""" + default_user_trafo_specs.amb_temp_surcharge = 0 + default_user_trafo_specs.top_oil_temp_rise = 60 + default_user_trafo_specs.winding_oil_gradient = 25 + default_user_trafo_specs.hot_spot_fac = 1.1 + default_user_trafo_specs.nom_load_sec_side = constant_load_profile_minutes.load_profile[0] * 5 + temp_threshold = CoolingSwitchConfig(activation_temp=60, deactivation_temp=50) + onan_parameters = ONANParameters( + top_oil_temp_rise=50.5, + time_const_oil=150, + time_const_windings=7, + load_loss=default_user_trafo_specs.load_loss, + nom_load_sec_side=constant_load_profile_minutes.load_profile[0] * 0.8, + winding_oil_gradient=23, + hot_spot_fac=1.2, + ) + onaf_switch = CoolingSwitchSettings( + fan_on=None, + temperature_threshold=temp_threshold, + onan_parameters=onan_parameters, + ) + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + model = Model(transformer=transformer, temperature_profile=constant_load_profile_minutes) + output = model.run() + + # The transformer now cools really fast in ONAF and heats up fast in ONAN + + # after warmup period, the top-oil temperature should be above 45 degrees + assert output.top_oil_temp_profile[20:].min() > 45 + + # at some point, the top-oil temperature should exceed the activation temperature and switch to ONAF, + # then it should cool down + assert output.top_oil_temp_profile.max() < 65 + + +def example_three_winding_onan_parameters(): + """Example of onan three winding alternative parameters.""" + return ThreeWindingONANParameters( + lv_winding=WindingSpecifications( + time_const_winding=10, nom_load=500, winding_oil_gradient=18, hot_spot_fac=1.1, nom_power=30 + ), + mv_winding=WindingSpecifications( + time_const_winding=10, nom_load=500, winding_oil_gradient=18, hot_spot_fac=1.1, nom_power=100 + ), + hv_winding=WindingSpecifications( + time_const_winding=10, nom_load=50, winding_oil_gradient=18, hot_spot_fac=1.1, nom_power=100 + ), + top_oil_temp_rise=55, + time_const_oil=160, + load_loss_mv_lv=100, + load_loss_hv_lv=100, + load_loss_hv_mv=100, + ) + + +def test_threewinding_onan_onaf_switch( + user_three_winding_transformer_specs: UserThreeWindingTransformerSpecifications, + three_winding_input_profile: ThreeWindingInputProfile, +): + """Check that a three-winding transformer can be created with an ONAF switch.""" + is_on = [False] * 50 + [True] * (len(three_winding_input_profile.datetime_index) - 50) + onan_parameters = example_three_winding_onan_parameters() + onaf_switch = ThreeWindingCoolingSwitchSettings( + fan_on=is_on, temperature_threshold=None, onan_parameters=onan_parameters + ) + transformer = ThreeWindingTransformer( + user_specs=user_three_winding_transformer_specs, + cooling_type=CoolerType.ONAF, + cooling_switch_settings=onaf_switch, + ) + model = Model(transformer=transformer, temperature_profile=three_winding_input_profile) + onan_onaf_results = model.run() + + # Check that an onan onaf switch with long periods of onaf reaches the same steady state as a constant onaf + onaf_transformer = ThreeWindingTransformer( + user_specs=user_three_winding_transformer_specs, cooling_type=CoolerType.ONAF + ) + onaf_model = Model(transformer=onaf_transformer, temperature_profile=three_winding_input_profile) + onaf_results = onaf_model.run() + + # At position 50 the ONAN-ONAF transformer should be warmer + assert onan_onaf_results.top_oil_temp_profile.iloc[50] > onaf_results.top_oil_temp_profile.iloc[50] + 10 + assert ( + onan_onaf_results.hot_spot_temp_profile["low_voltage_side"].iloc[-1] + > onaf_results.hot_spot_temp_profile["low_voltage_side"].iloc[-1] + ) + assert ( + onan_onaf_results.hot_spot_temp_profile["middle_voltage_side"].iloc[-1] + > onaf_results.hot_spot_temp_profile["middle_voltage_side"].iloc[-1] + ) + assert ( + onan_onaf_results.hot_spot_temp_profile["high_voltage_side"].iloc[-1] + > onaf_results.hot_spot_temp_profile["high_voltage_side"].iloc[-1] + ) + + # At the end it should be the same + assert math.isclose( + onaf_results.top_oil_temp_profile.iloc[-1], onan_onaf_results.top_oil_temp_profile.iloc[-1], rel_tol=1e-2 + ) + assert math.isclose( + onaf_results.hot_spot_temp_profile["low_voltage_side"].iloc[-1], + onan_onaf_results.hot_spot_temp_profile["low_voltage_side"].iloc[-1], + rel_tol=1e-2, + ) + assert math.isclose( + onaf_results.hot_spot_temp_profile["middle_voltage_side"].iloc[-1], + onan_onaf_results.hot_spot_temp_profile["middle_voltage_side"].iloc[-1], + rel_tol=1e-2, + ) + assert math.isclose( + onaf_results.hot_spot_temp_profile["high_voltage_side"].iloc[-1], + onan_onaf_results.hot_spot_temp_profile["high_voltage_side"].iloc[-1], + rel_tol=1e-2, + ) + + +def test_three_winding__onan_onaf_switch_threshold_temp( + user_three_winding_transformer_specs: UserThreeWindingTransformerSpecifications, + three_winding_input_profile: ThreeWindingInputProfile, +): + """Check that a three-winding transformer can be created with an ONAF switch based on temperature thresholds.""" + onan_parameters = example_three_winding_onan_parameters() + + # Use very low activation temps. to make it a ONAF transformer + onaf_switch = ThreeWindingCoolingSwitchSettings( + fan_on=None, + temperature_threshold=CoolingSwitchConfig(activation_temp=10, deactivation_temp=0), + onan_parameters=onan_parameters, + ) + transformer = ThreeWindingTransformer( + user_specs=user_three_winding_transformer_specs, + cooling_type=CoolerType.ONAF, + cooling_switch_settings=onaf_switch, + ) + model = Model(transformer=transformer, temperature_profile=three_winding_input_profile) + onan_onaf_results = model.run() + + onaf_transformer = ThreeWindingTransformer( + user_specs=user_three_winding_transformer_specs, cooling_type=CoolerType.ONAF + ) + onaf_model = Model(transformer=onaf_transformer, temperature_profile=three_winding_input_profile) + onaf_results = onaf_model.run() + + # They should be the same in all indices + assert onaf_results.top_oil_temp_profile.equals(onan_onaf_results.top_oil_temp_profile) + assert onaf_results.hot_spot_temp_profile["low_voltage_side"].equals( + onan_onaf_results.hot_spot_temp_profile["low_voltage_side"] + ) + assert onaf_results.hot_spot_temp_profile["middle_voltage_side"].equals( + onan_onaf_results.hot_spot_temp_profile["middle_voltage_side"] + ) + assert onaf_results.hot_spot_temp_profile["high_voltage_side"].equals( + onan_onaf_results.hot_spot_temp_profile["high_voltage_side"] + ) + + # Now we set a very high activation_temp, then it should be warmer in all but the first indice + onaf_switch = ThreeWindingCoolingSwitchSettings( + fan_on=None, + temperature_threshold=CoolingSwitchConfig(activation_temp=200, deactivation_temp=190), + onan_parameters=onan_parameters, + ) + transformer = ThreeWindingTransformer( + user_specs=user_three_winding_transformer_specs, + cooling_type=CoolerType.ONAF, + cooling_switch_settings=onaf_switch, + ) + model = Model(transformer=transformer, temperature_profile=three_winding_input_profile) + onan_onaf_results_2 = model.run() + + # Check that the temperatures are higher in all but the first index + assert (onan_onaf_results_2.top_oil_temp_profile.iloc[1:] > onaf_results.top_oil_temp_profile.iloc[1:]).all() + assert ( + onan_onaf_results_2.hot_spot_temp_profile["low_voltage_side"].iloc[1:] + > onaf_results.hot_spot_temp_profile["low_voltage_side"].iloc[1:] + ).all() + assert ( + onan_onaf_results_2.hot_spot_temp_profile["middle_voltage_side"].iloc[1:] + > onaf_results.hot_spot_temp_profile["middle_voltage_side"].iloc[1:] + ).all() + assert ( + onan_onaf_results_2.hot_spot_temp_profile["high_voltage_side"].iloc[1:] + > onaf_results.hot_spot_temp_profile["high_voltage_side"].iloc[1:] + ).all() + + +def test_switch_with_given_top_oil_temp( + default_user_trafo_specs: UserTransformerSpecifications, constant_load_profile_minutes +): + """Test switching logic when a top_oil temperature profile is given.""" + constant_top_oil_profile = np.array([80] * len(constant_load_profile_minutes.load_profile)) + constant_load_profile_minutes.top_oil_temperature_profile = constant_top_oil_profile + + temp_threshold_always_on = CoolingSwitchConfig(activation_temp=60, deactivation_temp=50) + temp_threshold_always_off = CoolingSwitchConfig(activation_temp=90, deactivation_temp=50) + + onan_parameters = ONANParameters( + top_oil_temp_rise=50.5, + time_const_oil=150, + time_const_windings=7, + load_loss=default_user_trafo_specs.load_loss, + nom_load_sec_side=constant_load_profile_minutes.load_profile[0] * 0.8, + winding_oil_gradient=23, + hot_spot_fac=1.2, + ) + onaf_switch = CoolingSwitchSettings( + fan_on=None, + temperature_threshold=temp_threshold_always_on, + onan_parameters=onan_parameters, + ) + transformer = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch + ) + model = Model(transformer=transformer, temperature_profile=constant_load_profile_minutes) + output = model.run() + + full_onaf_transformer = PowerTransformer(user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF) + full_onaf_model = Model(transformer=full_onaf_transformer, temperature_profile=constant_load_profile_minutes) + full_onaf_output = full_onaf_model.run() + + # Since the top-oil temperature is always above the activation temp, it should always be in ONAF mode + assert np.allclose(output.top_oil_temp_profile, full_onaf_output.top_oil_temp_profile) + assert np.allclose(output.hot_spot_temp_profile, full_onaf_output.hot_spot_temp_profile) + + onaf_switch_always_off = CoolingSwitchSettings( + fan_on=None, + temperature_threshold=temp_threshold_always_off, + onan_parameters=onan_parameters, + ) + transformer_onan = PowerTransformer( + user_specs=default_user_trafo_specs, + cooling_type=CoolerType.ONAF, + cooling_switch_settings=onaf_switch_always_off, + ) + model_onan = Model(transformer=transformer_onan, temperature_profile=constant_load_profile_minutes) + output_onan = model_onan.run() + + # ONAN mode is expected to be hotter than ONAF for all indices except the first, + # because the cooling fans in ONAF mode reduce the hot spot temperature after the initial state. + assert (output_onan.hot_spot_temp_profile.iloc[1:] > full_onaf_output.hot_spot_temp_profile.iloc[1:]).all() + + # Create a top-oil temperature profile that is constant at 90 for the first half and 70 for the second half + split_index = len(constant_load_profile_minutes.load_profile) // 2 + top_oil_temperature_profile = np.array([90] * split_index + [70] * split_index) + + constant_load_profile_minutes.top_oil_temperature_profile = top_oil_temperature_profile + onaf_switch_mixed = CoolingSwitchSettings( + fan_on=None, + temperature_threshold=CoolingSwitchConfig(activation_temp=85, deactivation_temp=75), + onan_parameters=onan_parameters, + ) + transformer_mixed = PowerTransformer( + user_specs=default_user_trafo_specs, cooling_type=CoolerType.ONAF, cooling_switch_settings=onaf_switch_mixed + ) + model_mixed = Model(transformer=transformer_mixed, temperature_profile=constant_load_profile_minutes) + output_mixed = model_mixed.run() + + model_mixed_onaf = Model(transformer=full_onaf_transformer, temperature_profile=constant_load_profile_minutes) + full_onaf_output_mixed = model_mixed_onaf.run() + + # In the first half, it should be in ONAF mode, in the second half in ONAN mode + assert np.allclose( + output_mixed.hot_spot_temp_profile.iloc[:split_index], + full_onaf_output_mixed.hot_spot_temp_profile.iloc[:split_index], + ) + assert ( + output_mixed.hot_spot_temp_profile.iloc[split_index + 1 :] + > full_onaf_output_mixed.hot_spot_temp_profile.iloc[split_index + 1 :] + ).all() diff --git a/transformer_thermal_model/model/thermal_model.py b/transformer_thermal_model/model/thermal_model.py index 69ab87e..485fdf6 100644 --- a/transformer_thermal_model/model/thermal_model.py +++ b/transformer_thermal_model/model/thermal_model.py @@ -8,8 +8,17 @@ import pandas as pd from transformer_thermal_model.schemas import OutputProfile -from transformer_thermal_model.schemas.thermal_model.input_profile import BaseInputProfile -from transformer_thermal_model.transformer import ThreeWindingTransformer, Transformer +from transformer_thermal_model.schemas.thermal_model.input_profile import ( + BaseInputProfile, + InputProfile, + ThreeWindingInputProfile, +) +from transformer_thermal_model.transformer import ( + DistributionTransformer, + PowerTransformer, + ThreeWindingTransformer, + Transformer, +) logger = logging.getLogger(__name__) @@ -56,7 +65,7 @@ class Model: Attributes: transformer (Transformer): The transformer that the model will use to calculate the temperatures. - data (pd.DataFrame): The data that the model will use to calculate the top-oil and hot-spottemperatures. + data (BaseInputProfile): The input profile that the model will use to calculate temperatures. init_top_oil_temp (float | None): The initial top-oil temperature. Defaults to None. If this is provided, will start the calculation with this temperature. If not provided, will start the calculation with the first value of the ambient temperature profile. @@ -81,7 +90,7 @@ def __init__( """Initialize the thermal model. Args: - temperature_profile (InputProfile): The temperature profile for the model. + temperature_profile (BaseInputProfile): The temperature profile for the model. transformer (Transformer): The transformer object. init_top_oil_temp (float | None): The initial top-oil temperature. Defaults to None. If this is provided, will start the calculation with this temperature. If not provided, will start the calculation @@ -99,14 +108,35 @@ def __init__( self.data = temperature_profile self.init_top_oil_temp = init_top_oil_temp + self.check_config() + + def check_config(self) -> None: + """Check if the combination of the transformer and input profile are valid.""" + if isinstance(self.transformer, ThreeWindingTransformer) and not isinstance( + self.data, ThreeWindingInputProfile + ): + raise ValueError("A ThreeWindingTransformer requires a ThreeWindingInputProfile.") + elif isinstance(self.transformer, PowerTransformer) and not isinstance(self.data, InputProfile): + raise ValueError("A PowerTransformer requires an InputProfile.") + elif isinstance(self.transformer, DistributionTransformer) and not isinstance(self.data, InputProfile): + raise ValueError("A DistributionTransformer requires an InputProfile.") + if ( + self.transformer.cooling_controller + and self.transformer.cooling_controller.onaf_switch.fan_on + and len(self.transformer.cooling_controller.onaf_switch.fan_on) != len(self.data) + ): + raise ValueError( + "The length of the fan_on list in the cooling_switch_settings must be equal to the length of the " + "temperature profile." + ) + def _get_time_step(self) -> np.ndarray: - """Get the time step between the data points. + """Get the time step between the data points in minutes. Returns: np.ndarray: The time step between the data points in minutes. """ - # Calculate time steps in minutes time_deltas = ( np.diff(self.data.datetime_index, prepend=self.data.datetime_index[0]) .astype("timedelta64[s]") @@ -126,20 +156,18 @@ def _get_internal_temp(self) -> np.ndarray: internal_temperature_profile = self.transformer._calculate_internal_temp(self.data.ambient_temperature_profile) return internal_temperature_profile - def _calculate_f1(self, dt: np.ndarray) -> np.ndarray: + def _calculate_f1(self, dt: float, time_const_oil: float) -> float: """Calculate the time delay constant f1 for the top-oil temperature.""" - return 1 - np.exp(-dt / (self.transformer.specs.oil_const_k11 * self.transformer.specs.time_const_oil)) + return 1 - np.exp(-dt / (self.transformer.specs.oil_const_k11 * time_const_oil)) - def _calculate_f2_winding(self, dt: np.ndarray) -> np.ndarray: + def _calculate_f2_winding(self, dt: float, time_const_windings_array: np.ndarray) -> np.ndarray: """Calculate the time delay constant f2 for the hot-spot temperature. due to the windings.""" - winding_delay = np.exp( - -dt / (self.transformer.specs.winding_const_k22 * self.transformer.specs.time_const_windings_array) - ) + winding_delay = np.exp(-dt / (self.transformer.specs.winding_const_k22 * time_const_windings_array)) return winding_delay - def _calculate_f2_oil(self, dt: np.ndarray) -> np.ndarray: + def _calculate_f2_oil(self, dt: float, time_const_oil: float) -> float: """Calculate the time delay constant f2 for the hot-spot temperature due to the oil.""" - oil_delay = np.exp(-dt * self.transformer.specs.winding_const_k22 / self.transformer.specs.time_const_oil) + oil_delay = np.exp(-dt * self.transformer.specs.winding_const_k22 / time_const_oil) return oil_delay def _calculate_static_hot_spot_increase(self, load: np.ndarray) -> np.ndarray: @@ -153,26 +181,38 @@ def _calculate_static_hot_spot_increase(self, load: np.ndarray) -> np.ndarray: def _calculate_top_oil_temp_profile( self, t_internal: np.ndarray, - f1: np.ndarray, - top_k: np.ndarray, + dt: np.ndarray, + load: np.ndarray, ) -> np.ndarray: """Calculate the top-oil temperature profile for the transformer. Args: t_internal (np.ndarray): Array of internal temperatures over time. - f1 (np.ndarray): Array of time constants for the top-oil temperature calculation. - top_k (np.ndarray): Array of end temperatures for the top-oil. + dt (np.ndarray): Array of time steps in minutes. + load (np.ndarray): Array of load values over time. Returns: np.ndarray: The computed top-oil temperature profile over time. """ top_oil_temp_profile = np.zeros_like(t_internal, dtype=np.float64) - top_oil_temp_profile[0] = t_internal[0] if self.init_top_oil_temp is None else self.init_top_oil_temp + top_oil_temp_profile[0] = self.init_top_oil_temp if self.init_top_oil_temp is not None else t_internal[0] + + self.transformer.set_ONAN_ONAF_first_timestamp(init_top_oil_temp=top_oil_temp_profile[0]) + # Handle both 1D (two-winding) and 2D (three-winding) load arrays for i in range(1, len(t_internal)): - top_oil_temp_profile[i] = self._update_top_oil_temp( - top_oil_temp_profile[i - 1], t_internal[i], top_k[i], f1[i] + f1 = self._calculate_f1(dt[i], self.transformer.specs.time_const_oil) + if load.ndim == 1: + top_k = self.transformer._end_temperature_top_oil(np.array([load[i]])) + else: + top_k = self.transformer._end_temperature_top_oil(load[:, i]) + top_oil_temp_profile[i] = self._update_top_oil_temp(top_oil_temp_profile[i - 1], t_internal[i], top_k, f1) + + new_specs = self.transformer.set_cooling_switch_controller_specs( + top_oil_temp_profile[i], top_oil_temp_profile[i - 1], i ) + if new_specs: + self.transformer.specs = new_specs return top_oil_temp_profile @@ -180,41 +220,51 @@ def _calculate_hot_spot_temp_profile( self, load: np.ndarray, top_oil_temp_profile: np.ndarray, - static_hot_spot_incr: np.ndarray, - f2_windings: np.ndarray, - f2_oil: np.ndarray, + dt: np.ndarray, ) -> np.ndarray: """Calculate the hot-spot temperature profile for the transformer. Args: load (np.ndarray): Array of load values over time. top_oil_temp_profile (np.ndarray): The computed top-oil temperature profile over time. - static_hot_spot_incr (np.ndarray): Array of static hot-spot temperature increases. - f2_windings (np.ndarray): Array of time constants for the hot-spot temperature calculation due to windings. - f2_oil (np.ndarray): Array of time constants for the hot-spot temperature calculation due to oil. + dt (np.ndarray): Array of time steps in minutes. Returns: np.ndarray: The computed hot-spot temperature profile over time. + - For two-winding transformers, returns a 1D array of shape (n_steps,). + - For three-winding transformers, returns a 2D array of shape (3, n_steps), + where each row corresponds to one winding: [low_voltage_side, middle_voltage_side, high_voltage_side]. """ - static_hot_spot_incr_windings = static_hot_spot_incr * self.transformer.specs.winding_const_k21 - static_hot_spot_incr_oil = static_hot_spot_incr * (self.transformer.specs.winding_const_k21 - 1) hot_spot_temp_profile = np.zeros_like(load, dtype=np.float64) # For a two winding transformer: if load.ndim == 1: + self.transformer.set_ONAN_ONAF_first_timestamp(init_top_oil_temp=top_oil_temp_profile[0]) hot_spot_temp_profile[0] = top_oil_temp_profile[0] hot_spot_increase_windings = np.zeros_like(load) hot_spot_increase_oil = np.zeros_like(load) for i in range(1, len(load)): + static_hot_spot_incr = self._calculate_static_hot_spot_increase(np.array([load[i]]))[0] + static_hot_spot_incr_windings = static_hot_spot_incr * self.transformer.specs.winding_const_k21 + static_hot_spot_incr_oil = static_hot_spot_incr * (self.transformer.specs.winding_const_k21 - 1) + + f2_windings = self._calculate_f2_winding(dt[i], self.transformer.specs.time_const_windings_array) + f2_oil = self._calculate_f2_oil(dt[i], self.transformer.specs.time_const_oil) + hot_spot_increase_windings[i] = self._update_hot_spot_increase( - hot_spot_increase_windings[i - 1], static_hot_spot_incr_windings[i], f2_windings[i] + hot_spot_increase_windings[i - 1], static_hot_spot_incr_windings, f2_windings[0] ) hot_spot_increase_oil[i] = self._update_hot_spot_increase( - hot_spot_increase_oil[i - 1], static_hot_spot_incr_oil[i], f2_oil[i] + hot_spot_increase_oil[i - 1], static_hot_spot_incr_oil, f2_oil ) hot_spot_temp_profile[i] = ( top_oil_temp_profile[i] + hot_spot_increase_windings[i] - hot_spot_increase_oil[i] ) + new_specs = self.transformer.set_cooling_switch_controller_specs( + top_oil_temp_profile[i], top_oil_temp_profile[i - 1], i + ) + if new_specs: + self.transformer.specs = new_specs # For a three winding transformer with multiple load profiles: else: @@ -222,20 +272,33 @@ def _calculate_hot_spot_temp_profile( n_profiles = load.shape[0] n_steps = load.shape[1] for profile in range(n_profiles): + self.transformer.set_ONAN_ONAF_first_timestamp(init_top_oil_temp=top_oil_temp_profile[0]) hot_spot_increase_windings = np.zeros(n_steps) hot_spot_increase_oil = np.zeros(n_steps) for i in range(1, n_steps): + static_hot_spot_incr = self._calculate_static_hot_spot_increase(load[:, i]) + static_hot_spot_incr_windings = static_hot_spot_incr * self.transformer.specs.winding_const_k21 + static_hot_spot_incr_oil = static_hot_spot_incr * (self.transformer.specs.winding_const_k21 - 1) + + f2_windings = self._calculate_f2_winding(dt[i], self.transformer.specs.time_const_windings_array) + f2_oil = self._calculate_f2_oil(dt[i], self.transformer.specs.time_const_oil) + hot_spot_increase_windings[i] = self._update_hot_spot_increase( hot_spot_increase_windings[i - 1], - static_hot_spot_incr_windings[profile][i], - f2_windings[profile][i], + static_hot_spot_incr_windings[profile], + f2_windings[profile].item(), ) hot_spot_increase_oil[i] = self._update_hot_spot_increase( - hot_spot_increase_oil[i - 1], static_hot_spot_incr_oil[profile][i], f2_oil[i] + hot_spot_increase_oil[i - 1], static_hot_spot_incr_oil[profile], f2_oil ) - hot_spot_temp_profile[profile][i] = ( + hot_spot_temp_profile[profile, i] = ( top_oil_temp_profile[i] + hot_spot_increase_windings[i] - hot_spot_increase_oil[i] ) + new_specs = self.transformer.set_cooling_switch_controller_specs( + top_oil_temp_profile[i], top_oil_temp_profile[i - 1], i + ) + if new_specs: + self.transformer.specs = new_specs return hot_spot_temp_profile @@ -274,24 +337,16 @@ def run(self, force_use_ambient_temperature: bool = False) -> OutputProfile: load = self.data.load_profile_array t_internal = self._get_internal_temp() - f1 = self._calculate_f1(dt) - f2_windings = self._calculate_f2_winding(dt) - f2_oil = self._calculate_f2_oil(dt) - top_k = self.transformer._end_temperature_top_oil(load) - static_hot_spot_incr = self._calculate_static_hot_spot_increase(load) - if use_top_oil and self.data.top_oil_temperature_profile is not None: top_oil_temp_profile = self.data.top_oil_temperature_profile else: - top_oil_temp_profile = self._calculate_top_oil_temp_profile(t_internal, f1, top_k) - hot_spot_temp_profile = self._calculate_hot_spot_temp_profile( - load, top_oil_temp_profile, static_hot_spot_incr, f2_windings, f2_oil - ) + top_oil_temp_profile = self._calculate_top_oil_temp_profile(t_internal, dt, load) + hot_spot_temp_profile = self._calculate_hot_spot_temp_profile(load, top_oil_temp_profile, dt) logger.info("The calculation with the Thermal model is completed.") logger.info(f"Max top-oil temperature: {np.max(top_oil_temp_profile)}") logger.info(f"Max hot-spot temperature: {np.max(hot_spot_temp_profile)}") - if type(self.transformer) is ThreeWindingTransformer: + if isinstance(self.transformer, ThreeWindingTransformer): return OutputProfile( top_oil_temp_profile=pd.Series(top_oil_temp_profile, index=self.data.datetime_index), hot_spot_temp_profile=pd.DataFrame( diff --git a/transformer_thermal_model/schemas/specifications/transformer.py b/transformer_thermal_model/schemas/specifications/transformer.py index df50abd..61b7983 100644 --- a/transformer_thermal_model/schemas/specifications/transformer.py +++ b/transformer_thermal_model/schemas/specifications/transformer.py @@ -295,9 +295,9 @@ def nominal_load_array(cls) -> np.ndarray: """Return the nominal loads as a numpy array.""" return np.array( [ - [cls.lv_winding.nom_load], - [cls.mv_winding.nom_load], - [cls.hv_winding.nom_load], + cls.lv_winding.nom_load, + cls.mv_winding.nom_load, + cls.hv_winding.nom_load, ] ) @@ -306,9 +306,9 @@ def winding_oil_gradient_array(cls) -> np.ndarray: """Return the winding oil gradient as a numpy array.""" return np.array( [ - [cls.lv_winding.winding_oil_gradient], - [cls.mv_winding.winding_oil_gradient], - [cls.hv_winding.winding_oil_gradient], + cls.lv_winding.winding_oil_gradient, + cls.mv_winding.winding_oil_gradient, + cls.hv_winding.winding_oil_gradient, ] ) @@ -317,9 +317,9 @@ def time_const_windings_array(cls) -> np.ndarray: """Return the winding oil gradient as a numpy array.""" return np.array( [ - [cls.lv_winding.time_const_winding], - [cls.mv_winding.time_const_winding], - [cls.hv_winding.time_const_winding], + cls.lv_winding.time_const_winding, + cls.mv_winding.time_const_winding, + cls.hv_winding.time_const_winding, ] ) @@ -328,8 +328,8 @@ def hot_spot_fac_array(cls) -> np.ndarray: """Return the winding oil gradient as a numpy array.""" return np.array( [ - [cls.lv_winding.hot_spot_fac], - [cls.mv_winding.hot_spot_fac], - [cls.hv_winding.hot_spot_fac], + cls.lv_winding.hot_spot_fac, + cls.mv_winding.hot_spot_fac, + cls.hv_winding.hot_spot_fac, ] ) diff --git a/transformer_thermal_model/schemas/thermal_model/__init__.py b/transformer_thermal_model/schemas/thermal_model/__init__.py index 8dc3bcc..dd17798 100644 --- a/transformer_thermal_model/schemas/thermal_model/__init__.py +++ b/transformer_thermal_model/schemas/thermal_model/__init__.py @@ -3,6 +3,24 @@ # SPDX-License-Identifier: MPL-2.0 from .input_profile import InputProfile, ThreeWindingInputProfile +from .onaf_switch import ( + CoolingSwitchBase, + CoolingSwitchConfig, + CoolingSwitchSettings, + ONANParameters, + ThreeWindingCoolingSwitchSettings, + ThreeWindingONANParameters, +) from .output_profile import OutputProfile -__all__ = ["InputProfile", "OutputProfile", "ThreeWindingInputProfile"] +__all__ = [ + "InputProfile", + "OutputProfile", + "ThreeWindingInputProfile", + "CoolingSwitchBase", + "CoolingSwitchConfig", + "CoolingSwitchSettings", + "ONANParameters", + "ThreeWindingCoolingSwitchSettings", + "ThreeWindingONANParameters", +] diff --git a/transformer_thermal_model/schemas/thermal_model/onaf_switch.py b/transformer_thermal_model/schemas/thermal_model/onaf_switch.py new file mode 100644 index 0000000..70e2f6d --- /dev/null +++ b/transformer_thermal_model/schemas/thermal_model/onaf_switch.py @@ -0,0 +1,102 @@ +# SPDX-FileCopyrightText: Contributors to the Transformer Thermal Model project +# +# SPDX-License-Identifier: MPL-2.0 + +from typing import Self + +from pydantic import BaseModel, Field, model_validator + +from transformer_thermal_model.schemas.specifications.transformer import WindingSpecifications + + +class CoolingSwitchConfig(BaseModel): + """Class representing the fan switch configuration for ONAF cooling.""" + + activation_temp: float = Field(..., description="Temperature at which the fan cooling activates.") + deactivation_temp: float = Field(..., description="Temperature at which the fan cooling deactivates.") + + @model_validator(mode="after") + def check_temperatures(self) -> Self: + """Check that the activation temperature is higher than the deactivation temperature.""" + if self.activation_temp <= self.deactivation_temp: + raise ValueError("Activation temperature must be higher than deactivation temperature.") + return self + + +class BaseONANParameters(BaseModel): + """Base class representing common ONAN (Oil Natural Air Natural) cooling parameters. + + This is used when an ONAF transformer switches to ONAN cooling. + """ + + top_oil_temp_rise: float + time_const_oil: float + + +class ONANParameters(BaseONANParameters): + """Class representing ONAN (Oil Natural Air Natural) cooling parameters.""" + + time_const_windings: float + load_loss: float + nom_load_sec_side: float + winding_oil_gradient: float + hot_spot_fac: float + + +class ThreeWindingONANParameters(BaseONANParameters): + """Class representing ONAN (Oil Natural Air Natural) cooling parameters for three-winding transformers.""" + + lv_winding: WindingSpecifications = Field(..., description="ONAN parameters for the LV winding.") + mv_winding: WindingSpecifications = Field(..., description="ONAN parameters for the MV winding.") + hv_winding: WindingSpecifications = Field(..., description="ONAN parameters for the HV winding.") + load_loss_mv_lv: float + load_loss_hv_lv: float + load_loss_hv_mv: float + + +class CoolingSwitchBase(BaseModel): + """Class representing the ONAF (Oil Natural Air Forced) cooling switch status.""" + + fan_on: list[bool] | None = Field( + None, description="List indicating the ONAF cooling switch status at each time step." + ) + temperature_threshold: CoolingSwitchConfig | None = Field( + None, description="Temperature threshold for activating the ONAF cooling switch." + ) + + @model_validator(mode="after") + def check_consistency(self) -> Self: + """Check that either fan_on or temperature_threshold is provided, but not both. + + There are two ways to model a switch between ON and OFF for the ONAF cooling: + 1. Provide a list of booleans indicating whether the switch is ON (True) or OFF (False) at each time step. + 2. Provide a temperature threshold, where the switch turns ON when the hot-spot temperature + exceeds this threshold. + """ + if self.fan_on is not None and self.temperature_threshold is not None: + raise ValueError("Provide either 'fan_on' or 'temperature_threshold', not both.") + if self.fan_on is None and self.temperature_threshold is None: + raise ValueError("Either 'fan_on' or 'temperature_threshold' must be provided.") + return self + + +class CoolingSwitchSettings(CoolingSwitchBase): + """Class representing the ONAF (Oil Natural Air Forced) cooling switch settings. + + This class includes the ONAN parameters to be used when the transformer switches to ONAN cooling. + """ + + onan_parameters: ONANParameters = Field( + ..., description="ONAN parameters to be used when the transformer switches to ONAN cooling." + ) + + +class ThreeWindingCoolingSwitchSettings(CoolingSwitchBase): + """Class representing the ONAF (Oil Natural Air Forced) cooling switch settings for three-winding transformers. + + This class includes the ONAN parameters to be used when the transformer switches to ONAN cooling. + """ + + onan_parameters: ThreeWindingONANParameters = Field( + ..., description="ONAN parameters to be used when the transformer switches to ONAN cooling." + ) diff --git a/transformer_thermal_model/transformer/base.py b/transformer_thermal_model/transformer/base.py index 4f05fe8..7710e15 100644 --- a/transformer_thermal_model/transformer/base.py +++ b/transformer_thermal_model/transformer/base.py @@ -11,6 +11,7 @@ BaseDefaultTransformerSpecifications, BaseTransformerSpecifications, ) +from transformer_thermal_model.transformer.cooling_switch_controller import CoolingSwitchController class Transformer(ABC): @@ -29,19 +30,33 @@ class Transformer(ABC): cooling_type: CoolerType specs: BaseTransformerSpecifications - def __init__( - self, - cooling_type: CoolerType, - ): + def __init__(self, cooling_type: CoolerType, cooling_controller: CoolingSwitchController | None = None): """Initialize the Transformer object. Args: - user_specs (UserTransformerSpecifications): The transformer specifications that you need to - provide to build the transformer. Any optional specifications not provided will be taken from the - default specifications. cooling_type (CoolerType): The cooling type. Can be ONAN, ONAF. + cooling_controller (CoolingSwitchController | None): The cooling controller that handles the ONAN/ONAF + switching logic. """ self.cooling_type: CoolerType = cooling_type + if cooling_type == CoolerType.ONAN and cooling_controller is not None: + raise ValueError("ONAF switch only works when the cooling type is ONAF.") + self.cooling_controller = cooling_controller + + def set_ONAN_ONAF_first_timestamp(self, init_top_oil_temp: float) -> None: + """Delegate initial cooling type logic to CoolingSwitchController.""" + if self.cooling_controller: + self.specs = self.cooling_controller.determine_initial_specifications( + initial_top_oil_temperature=init_top_oil_temp + ) + + def set_cooling_switch_controller_specs( + self, top_oil_temp: float, previous_top_oil_temp: float, index: int + ) -> BaseTransformerSpecifications | None: + """Delegate ONAN/ONAF switch logic to CoolingSwitchController.""" + if self.cooling_controller: + return self.cooling_controller.get_new_specs(top_oil_temp, previous_top_oil_temp, index) + return None @property @abstractmethod @@ -59,5 +74,5 @@ def _calculate_internal_temp(self, ambient_temperature: np.ndarray) -> np.ndarra pass @abstractmethod - def _end_temperature_top_oil(self, load: np.ndarray) -> np.ndarray: + def _end_temperature_top_oil(self, load: np.ndarray) -> float: pass diff --git a/transformer_thermal_model/transformer/cooling_switch_controller.py b/transformer_thermal_model/transformer/cooling_switch_controller.py new file mode 100644 index 0000000..51d49a9 --- /dev/null +++ b/transformer_thermal_model/transformer/cooling_switch_controller.py @@ -0,0 +1,210 @@ +# SPDX-FileCopyrightText: Contributors to the Transformer Thermal Model project +# +# SPDX-License-Identifier: MPL-2.0 + +from transformer_thermal_model.schemas.specifications.transformer import ( + BaseTransformerSpecifications, + ThreeWindingTransformerSpecifications, + TransformerSpecifications, +) +from transformer_thermal_model.schemas.thermal_model.onaf_switch import ( + CoolingSwitchBase, + CoolingSwitchConfig, + CoolingSwitchSettings, + ThreeWindingCoolingSwitchSettings, +) + + +class CoolingSwitchController: + """Encapsulates ONAN/ONAF cooling switch logic for transformers. + + This class manages the automatic switching between ONAN (Oil Natural Air Natural) and ONAF + (Oil Natural Air Forced) cooling modes based on either: + - A predefined fan status schedule (list of boolean values) + - Temperature thresholds (activation and deactivation temperatures) + + The controller is used internally by transformer classes and handles the logic for determining + when to switch cooling modes and what specifications to apply for each mode. + + Example: Using CoolingSwitchController with temperature-based switching + ```python + >>> from transformer_thermal_model.transformer import PowerTransformer + >>> from transformer_thermal_model.schemas import UserTransformerSpecifications + >>> from transformer_thermal_model.schemas.thermal_model import ( + ... CoolingSwitchSettings, + ... CoolingSwitchConfig, + ... ONANParameters, + ... ) + >>> from transformer_thermal_model.cooler import CoolerType + + >>> # Define the transformer specifications for ONAF mode + >>> user_specs = UserTransformerSpecifications( + ... load_loss=1000, + ... nom_load_sec_side=1500, + ... no_load_loss=200, + ... amb_temp_surcharge=20, + ... ) + >>> # Define ONAN parameters (for when fans are off) + >>> onan_params = ONANParameters( + ... nom_load_sec_side=1200, + ... top_oil_temp_rise=65, + ... winding_oil_gradient=20, + ... hot_spot_fac=1.3, + ... time_const_oil=210, + ... time_const_windings=10, + ... load_loss=800, + ... ) + >>> # Create switch configuration with temperature thresholds + >>> # Fans activate at 70°C, deactivate at 60°C + >>> onaf_switch = CoolingSwitchSettings( + ... temperature_threshold=CoolingSwitchConfig(activation_temp=70, deactivation_temp=60), + ... onan_parameters=onan_params + ... ) + >>> # Create transformer with automatic switching capability + >>> transformer = PowerTransformer( + ... user_specs=user_specs, + ... cooling_type=CoolerType.ONAF, + ... cooling_switch_settings=onaf_switch + ... ) + >>> # The CoolingSwitchController is now managing the cooling mode switches automatically + + ``` + + Example: Using CoolingSwitchController with predefined fan status + ```python + >>> from transformer_thermal_model.transformer import PowerTransformer + >>> from transformer_thermal_model.schemas.thermal_model import CoolingSwitchSettings, ONANParameters + + >>> # Create a fan status schedule: True = ONAF (fans on), False = ONAN (fans off) + >>> # This represents 5 time steps with fans on, then off, then on again + >>> fan_schedule = [True, True, True, True, True, False, False, False, True, True] + >>> # Define ONAN parameters for when fans are off + >>> onan_params = ONANParameters( + ... nom_load_sec_side=1200, + ... top_oil_temp_rise=65, + ... winding_oil_gradient=20, + ... hot_spot_fac=1.3, + ... time_const_oil=210, + ... time_const_windings=10, + ... load_loss=800, + ... ) + >>> onaf_switch = CoolingSwitchSettings( + ... fan_on=fan_schedule, + ... onan_parameters=onan_params + ... ) + >>> transformer = PowerTransformer( + ... user_specs=user_specs, + ... cooling_type=CoolerType.ONAF, + ... cooling_switch_settings=onaf_switch + ... ) + >>> # The controller will switch modes according to the predefined schedule + + ``` + + Attributes: + onaf_switch (CoolingSwitchSettings | ThreeWindingCoolingSwitchSettings): The switch configuration containing + either fan status schedule or temperature thresholds, plus ONAN parameters. + original_onaf_specs (BaseTransformerSpecifications): Deep copy of the original ONAF specifications, + used as reference when switching back to ONAF mode. + """ + + def __init__( + self, + onaf_switch: CoolingSwitchBase, + specs: BaseTransformerSpecifications, + ): + """Initialize the controller with the given ONAF switch settings and transformer specifications.""" + self.onaf_switch = onaf_switch + self.original_onaf_specs = specs.model_copy(deep=True) + + def determine_initial_specifications( + self, + initial_top_oil_temperature: float, + ) -> BaseTransformerSpecifications: + """Get the initial specifications based on the ONAF switch settings. + + If the fans are off at the start or if a temperature threshold is set, + the transformer starts with ONAN specifications. Otherwise, it starts with ONAF specifications. + """ + if self.onaf_switch.fan_on is not None: + if not self.onaf_switch.fan_on[0]: + return self.create_onan_specifications() + elif ( + self.onaf_switch.temperature_threshold is not None + and initial_top_oil_temperature < self.onaf_switch.temperature_threshold.activation_temp + ): + return self.create_onan_specifications() + return self.original_onaf_specs + + def create_onan_specifications(self) -> BaseTransformerSpecifications: + """Create ONAN specifications by merging ONAN parameters into the original ONAF specifications. + + This method returns a deep copy of the original ONAF specifications with fields overridden by the + ONAN parameters provided in the switch configuration. Only fields present in the ONAN parameters + are updated; all other fields remain unchanged. The method automatically selects the correct + specification type (standard or three-winding) based on the input objects. + """ + transformer_specs = self.original_onaf_specs.model_copy(deep=True) + + if isinstance(transformer_specs, TransformerSpecifications) and isinstance( + self.onaf_switch, CoolingSwitchSettings + ): + specs_dict = transformer_specs.model_dump() + specs_dict.update(self.onaf_switch.onan_parameters.model_dump(exclude_none=True)) + transformer_specs = TransformerSpecifications(**specs_dict) + + elif isinstance(transformer_specs, ThreeWindingTransformerSpecifications) and isinstance( + self.onaf_switch, ThreeWindingCoolingSwitchSettings + ): + specs_dict = transformer_specs.model_dump() + specs_dict.update(self.onaf_switch.onan_parameters.model_dump(exclude_none=True)) + transformer_specs = ThreeWindingTransformerSpecifications(**specs_dict) + + return transformer_specs + + def get_new_specs( + self, top_oil_temp: float, previous_top_oil_temp: float, index: int + ) -> BaseTransformerSpecifications | None: + """Check and handle the ONAF/ONAN switch based on the top-oil temperature and the switch settings. + + This method evaluates the current and previous top-oil temperatures, along with the fan status + and temperature thresholds, to determine the appropriate transformer specifications to use. + + Args: + top_oil_temp (float): Current top-oil temperature. + previous_top_oil_temp (float): Previous top-oil temperature. + index (int): Index for fan status or threshold evaluation. + """ + fan_on = self.onaf_switch.fan_on + temp_threshold = self.onaf_switch.temperature_threshold + + if fan_on is not None and index < len(fan_on) - 1: + return self._handle_fan_status_switch(fan_on, index) + elif temp_threshold is not None: + return self._handle_temp_threshold_switch(temp_threshold, top_oil_temp, previous_top_oil_temp) + return None + + def _handle_fan_status_switch(self, fan_on: list[bool], index: int) -> BaseTransformerSpecifications | None: + """Handle switching based on fan status list.""" + previous_fan_status, current_fan_status = fan_on[index], fan_on[index + 1] + if previous_fan_status != current_fan_status: + if current_fan_status: + return self.original_onaf_specs + else: + return self.create_onan_specifications() + return None + + def _handle_temp_threshold_switch( + self, temp_threshold: CoolingSwitchConfig, top_oil_temp: float, previous_top_oil_temp: float + ) -> BaseTransformerSpecifications | None: + """Handle switching based on temperature thresholds. + + This method evaluates the current and previous top-oil temperatures against the activation and + deactivation thresholds to determine the appropriate transformer specifications to use. + """ + activation_temp, deactivation_temp = temp_threshold.activation_temp, temp_threshold.deactivation_temp + if previous_top_oil_temp < activation_temp <= top_oil_temp: + return self.original_onaf_specs + elif previous_top_oil_temp > deactivation_temp >= top_oil_temp: + return self.create_onan_specifications() + return None diff --git a/transformer_thermal_model/transformer/distribution.py b/transformer_thermal_model/transformer/distribution.py index e7da47d..c3b4b34 100644 --- a/transformer_thermal_model/transformer/distribution.py +++ b/transformer_thermal_model/transformer/distribution.py @@ -72,6 +72,7 @@ def __init__( super().__init__( cooling_type=CoolerType.ONAN, + cooling_controller=None, ) self.specs = TransformerSpecifications.create(self.defaults, user_specs) @@ -96,9 +97,14 @@ def defaults(self) -> DefaultTransformerSpecifications: def _pre_factor(self) -> float: return self.specs.top_oil_temp_rise + self.specs.amb_temp_surcharge - def _end_temperature_top_oil(self, load: np.ndarray) -> np.ndarray: - """Calculate the end temperature of the top-oil.""" - load_ratio = np.power(load / self.specs.nom_load_sec_side, 2) + def _end_temperature_top_oil(self, load: np.ndarray) -> float: + """Calculate the end temperature of the top-oil. + + The load is expected to be a 1D array with a single value for a power transformer. This is to keep the + interface consistent with the three-winding transformer, which can have multiple load profiles. In the + code we therefore access the first element of the array. + """ + load_ratio = np.power(load[0] / self.specs.nom_load_sec_side, 2) total_loss_ratio = (self.specs.no_load_loss + self.specs.load_loss * load_ratio) / ( self.specs.no_load_loss + self.specs.load_loss ) diff --git a/transformer_thermal_model/transformer/power.py b/transformer_thermal_model/transformer/power.py index d250ef3..33e3fd7 100644 --- a/transformer_thermal_model/transformer/power.py +++ b/transformer_thermal_model/transformer/power.py @@ -15,6 +15,8 @@ TransformerSpecifications, UserTransformerSpecifications, ) +from transformer_thermal_model.schemas.thermal_model import CoolingSwitchSettings +from transformer_thermal_model.transformer.cooling_switch_controller import CoolingSwitchController from .base import Transformer @@ -133,6 +135,7 @@ def __init__( user_specs: UserTransformerSpecifications, cooling_type: CoolerType, internal_component_specs: TransformerComponentSpecifications | None = None, + cooling_switch_settings: CoolingSwitchSettings | None = None, ): """Initialize the transformer object. @@ -143,21 +146,31 @@ def __init__( cooling_type (CoolerType): The cooling type. Can be ONAN or ONAF. internal_component_specs (TransformerComponentSpecifications, optional): The internal component specifications, which are used to calculate the limiting component. Defaults to None. + cooling_switch_settings (CoolingSwitchSettings, optional): The ONAF switch settings. + Only used when the cooling type is ONAF. """ logger.info("Creating a power transformer object.") logger.info("User transformer specifications: %s", user_specs) logger.info("Cooling type: %s", cooling_type) + self.cooling_type: CoolerType = cooling_type + if internal_component_specs is not None: logger.info("Internal component specifications: %s", internal_component_specs) self.internal_component_specs = internal_component_specs - super().__init__( - cooling_type=cooling_type, - ) self.specs = TransformerSpecifications.create(self.defaults, user_specs) + # Use CoolingSwitchController if cooling_switch_settings is provided + self.cooling_controller = ( + CoolingSwitchController(onaf_switch=cooling_switch_settings, specs=self.specs) + if cooling_switch_settings + else None + ) + + super().__init__(cooling_type=cooling_type, cooling_controller=self.cooling_controller) + @property def defaults(self) -> DefaultTransformerSpecifications: """The ClassVar for default TransformerSpecifications. @@ -276,9 +289,14 @@ def int_cur_trans_capacity_ratio(self) -> float | None: return ct_load / nominal_load - def _end_temperature_top_oil(self, load: np.ndarray) -> np.ndarray: - """Calculate the end temperature of the top-oil.""" - load_ratio = np.power(load / self.specs.nom_load_sec_side, 2) + def _end_temperature_top_oil(self, load: np.ndarray) -> float: + """Calculate the end temperature of the top-oil. + + The load is expected to be a 1D array with a single value for a power transformer. This is to keep the + interface consistent with the three-winding transformer, which can have multiple load profiles. In the + code we therefore access the first element of the array. + """ + load_ratio = np.power(load[0] / self.specs.nom_load_sec_side, 2) total_loss_ratio = (self.specs.no_load_loss + self.specs.load_loss * load_ratio) / ( self.specs.no_load_loss + self.specs.load_loss ) diff --git a/transformer_thermal_model/transformer/threewinding.py b/transformer_thermal_model/transformer/threewinding.py index 200d612..7568c93 100644 --- a/transformer_thermal_model/transformer/threewinding.py +++ b/transformer_thermal_model/transformer/threewinding.py @@ -12,6 +12,8 @@ ThreeWindingTransformerSpecifications, UserThreeWindingTransformerSpecifications, ) +from transformer_thermal_model.schemas.thermal_model.onaf_switch import ThreeWindingCoolingSwitchSettings +from transformer_thermal_model.transformer.cooling_switch_controller import CoolingSwitchController from .base import Transformer @@ -82,13 +84,25 @@ def __init__( self, user_specs: UserThreeWindingTransformerSpecifications, cooling_type: CoolerType, + cooling_switch_settings: ThreeWindingCoolingSwitchSettings | None = None, ): """Initialize the ThreeWindingTransformer object.""" + logger.debug("Initialized ThreeWindingTransformer with specifications: %s", user_specs) + + self.cooling_type: CoolerType = cooling_type + self.specs = ThreeWindingTransformerSpecifications.create(self.defaults, user_specs) + + # Use CoolingSwitchController if onaf_switch is provided + self.cooling_controller = ( + CoolingSwitchController(onaf_switch=cooling_switch_settings, specs=self.specs) + if cooling_switch_settings + else None + ) + super().__init__( cooling_type=cooling_type, + cooling_controller=self.cooling_controller, ) - self.specs = ThreeWindingTransformerSpecifications.create(self.defaults, user_specs) - logger.debug("Initialized ThreeWindingTransformer with specifications: %s", user_specs) @property def defaults(self) -> ThreeWindingTransformerDefaultSpecifications: @@ -106,7 +120,7 @@ def _calculate_internal_temp(self, ambient_temperature: np.ndarray) -> np.ndarra """Calculate the internal temperature of the transformer.""" return ambient_temperature + self.specs.amb_temp_surcharge - def _end_temperature_top_oil(self, load_profile: np.ndarray) -> np.ndarray: + def _end_temperature_top_oil(self, load_profile: np.ndarray) -> float: """Calculate the end temperature of the top-oil.""" lv_rise = self.specs._get_loss_lc() * np.power(load_profile[0] / self.specs.lv_winding.nom_load, 2) mv_rise = self.specs._get_loss_mc() * np.power(load_profile[1] / self.specs.mv_winding.nom_load, 2)