{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Experiment: Combine Models\n",
"\n",
"In this notebook, you can combine the difference models of py_wake and see the effects in terms of AEP and a flow map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Install PyWake if needed\n",
"try:\n",
" import py_wake\n",
"except ModuleNotFoundError:\n",
" !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake.git"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import all available models\n",
"from py_wake.deficit_models import *\n",
"from py_wake.wind_farm_models import *\n",
"from py_wake.rotor_avg_models import *\n",
"from py_wake.superposition_models import *\n",
"from py_wake.deflection_models import *\n",
"from py_wake.turbulence_models import *\n",
"from py_wake.ground_models import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Setup site, wind turbines\n",
"from py_wake.examples.data.iea37._iea37 import IEA37Site, IEA37_WindTurbines\n",
"site = IEA37Site(16)\n",
"windTurbines = IEA37_WindTurbines()\n",
"x,y = site.initial_position.T"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>.widget-label { min-width: 20ex !important; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# prepare for the model combination tool\n",
"from py_wake.utils.model_utils import get_models, get_signature\n",
"from ipywidgets import interact\n",
"from IPython.display import HTML, display, Javascript\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Fix ipywidget label width\n",
"display(HTML('''<style>.widget-label { min-width: 20ex !important; }</style>'''))\n",
"\n",
"\n",
"def print_signature(windFarmModel, **kwargs):\n",
" s = \"\"\"# windFarmModel autogenerated by dropdown boxes\n",
"t = time.time()\n",
"wfm = %s\n",
"sim_res = wfm(x,y)\n",
"plt.figure(figsize=(12,8))\n",
"sim_res.flow_map(wd=270).plot_wake_map()\n",
"print (wfm)\n",
"print (\"Computation time (AEP + flowmap):\", time.time()-t)\n",
"plt.title('AEP: %%.2fGWh'%%(sim_res.aep().sum()))\n",
"\"\"\"% get_signature(windFarmModel, kwargs, 1)\n",
" \n",
" # Write windFarmModel code to cell starting \"# windFarmModel autogenerated by dropdown boxes\"\n",
" display(Javascript(\"\"\"\n",
"for (var cell of IPython.notebook.get_cells()) {\n",
" if (cell.get_text().startsWith(\"# windFarmModel autogenerated by dropdown boxes\")){\n",
" cell.set_text(`%s`);\n",
" cell.execute();\n",
" }\n",
"}\"\"\"%s))\n",
"\n",
"# setup list of models\n",
"models = {n:[(getattr(m,'__name__',m), m) for m in get_models(cls)] \n",
" for n,cls in [('windFarmModel', WindFarmModel),\n",
" ('wake_deficitModel', WakeDeficitModel),\n",
" ('rotorAvgModel', RotorAvgModel),\n",
" ('superpositionModel', SuperpositionModel),\n",
" ('blockage_deficitModel', BlockageDeficitModel),\n",
" ('deflectionModel',DeflectionModel),\n",
" ('turbulenceModel', TurbulenceModel),\n",
" ('groundModel', GroundModel)\n",
" ]}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Combine and execute model\n",
"\n",
"Combine your model via the dropdown boxes below\n",
"Choosing a different model updates and executes the the code cell below which runs the wind farm model, prints the AEP and plots a flow map"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "171330f143a1445b9c63829ba26c8455",
"version_major": 2,
"version_minor": 0
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"text/plain": [
"interactive(children=(Dropdown(description='windFarmModel', options=(('PropagateDownwind', <class 'py_wake.win…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = interact(print_signature, **models)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-7-5850474ce68c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# windFarmModel autogenerated by dropdown boxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m wfm = PropagateDownwind(\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0msite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mwindTurbines\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\mmpe\\programming\\python\\topfarm\\pywake\\py_wake\\wind_farm_models\\engineering_models.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, site, windTurbines, wake_deficitModel, rotorAvgModel, superpositionModel, deflectionModel, turbulenceModel, groundModel)\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[0mModel\u001b[0m \u001b[0mdescribing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mamount\u001b[0m \u001b[0mof\u001b[0m \u001b[0madded\u001b[0m \u001b[0mturbulence\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mwake\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 392\u001b[0m \"\"\"\n\u001b[1;32m--> 393\u001b[1;33m EngineeringWindFarmModel.__init__(self, site, windTurbines, wake_deficitModel, rotorAvgModel, superpositionModel,\n\u001b[0m\u001b[0;32m 394\u001b[0m \u001b[0mblockage_deficitModel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeflectionModel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdeflectionModel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 395\u001b[0m turbulenceModel=turbulenceModel, groundModel=groundModel)\n",
"\u001b[1;32mc:\\mmpe\\programming\\python\\topfarm\\pywake\\py_wake\\wind_farm_models\\engineering_models.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, site, windTurbines, wake_deficitModel, rotorAvgModel, superpositionModel, blockage_deficitModel, deflectionModel, turbulenceModel, groundModel)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwake_deficitModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeficitModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrotorAvgModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRotorAvgModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 53\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msuperpositionModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSuperpositionModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 54\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mblockage_deficitModel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblockage_deficitModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeficitModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mdeflectionModel\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeflectionModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDeflectionModel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"# windFarmModel autogenerated by dropdown boxes\n",
"t = time.time()\n",
"wfm = PropagateDownwind(\n",
" site,\n",
" windTurbines,\n",
" wake_deficitModel=NOJDeficit(\n",
" k=0.1,\n",
" use_effective_ws=False),\n",
" rotorAvgModel=RotorCenter(),\n",
" superpositionModel=LinearSum(),\n",
" deflectionModel=None,\n",
" turbulenceModel=None,\n",
" groundModel=NoGround())\n",
"sim_res = wfm(x,y)\n",
"plt.figure(figsize=(12,8))\n",
"sim_res.flow_map(wd=270).plot_wake_map()\n",
"print (wfm)\n",
"print (\"Computation time (AEP + flowmap):\", time.time()-t)\n",
"plt.title('AEP: %.2fGWh'%(sim_res.aep().sum()))\n"
]
},
{
"cell_type": "code",
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