diff --git a/docs/notebooks/WindTurbines.ipynb b/docs/notebooks/WindTurbines.ipynb index e5dfdd642e1bc54fca1b059e7e05a51de8f0742a..33098854ca643dd7a3f08bb2743dd520b05f7a2b 100644 --- a/docs/notebooks/WindTurbines.ipynb +++ b/docs/notebooks/WindTurbines.ipynb @@ -14,6 +14,13 @@ "For a given wind turbine type and effective wind speed (WSeff), the `WindTurbines` object provides the power and thrust coefficient (CT), as well as the wind turbine hub height (H) and diameter (D)." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up Windturbine objects" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -102,7 +109,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Multitype Wind Turbines\n", + "## Multi-type Wind Turbines\n", "You can collect a list of different turbine types into a single WindTurbines object" ] }, @@ -119,26 +126,6 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'MyWT'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wts.name(type=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -166,16 +153,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x25a6d4fc160>" + "<matplotlib.legend.Legend at 0x25fce538a30>" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -210,16 +197,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x25a6e953d60>" + "<matplotlib.legend.Legend at 0x25fce8b3070>" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -248,13 +235,310 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Plot" + "## Multidimensional Power/Ct curves" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some WAsP wtg files defines multiple wind turbine modes. E.g. the `Vestas V112-3.0 MW.wtg` which has 12 modes representing different levels of air density. In this case, the mode defaults to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x25fcd44d9d0>" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "wtg_file = os.path.join(wtg_path, 'Vestas V112-3.0 MW.wtg')\n", + "v112 = WindTurbines.from_WAsP_wtg(wtg_file)\n", + "required_inputs, optional_inputs = v112.power_ct_inputs\n", + "upct = {}\n", + "for m in [0,1,5]:\n", + " plt.plot(v112.power(ws, mode=m)/1000, label=f'mode: {m}')\n", + "\n", + "p0,ct0 = v112.power_ct(ws, mode=0)\n", + "p1,ct1 = v112.power_ct(ws, mode=1)\n", + "\n", + "plt.ylabel('Power [kW]')\n", + "plt.xlabel('Wind speed [m/s]')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discrete dimensions (e.g. operational mode)\n", + "WindTurbines can be defined using a `PowerCtFunctionList`. In fact this is the approach used by multi-mode WAsP wind turbines and also when creating multi-type wind turbine (in which case the key is `type`)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x25fce978e80>" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from py_wake.wind_turbines.power_ct_functions import PowerCtFunctionList\n", + "\n", + "mode0_power_ct=PowerCtTabular(ws, p0, 'w', ct0)\n", + "mode1_power_ct=PowerCtTabular(ws, p1, 'w', ct1)\n", + "\n", + "multimode_power_ct = PowerCtFunctionList(key='mode', \n", + " powerCtFunction_lst=[mode0_power_ct, mode1_power_ct], \n", + " default_value=None)\n", + "wt = WindTurbine('MultimodeWT', 112, 84, powerCtFunction=multimode_power_ct)\n", + "\n", + "for m in [0,1]:\n", + " plt.plot(wt.power(ws, mode=m)/1000, label=f'mode: {m}')\n", + "plt.ylabel('Power [kW]')\n", + "plt.xlabel('Wind speed [m/s]')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multidimentional power/ct tabulars\n", + "It is also possible to setup a wind turbine using a multidimensional power and ct tabular array. In this case the power can ct values will be calculated usign multidimensional linear interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x25fce9e1340>" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from py_wake.wind_turbines.power_ct_functions import PowerCtNDTabular\n", + "\n", + "# setup multidimensional power and ct tabulars\n", + "# p0,ct0 ~ rho=0.95\n", + "# p1,ct1 ~ rho=1.225\n", + "\n", + "power_array = np.array([p1,p0]).T\n", + "ct_array = np.array([ct1,ct0]).T\n", + "density = [0.95,1.225]\n", + "powerCtFunction = PowerCtNDTabular(input_keys=['ws','rho'], \n", + " value_lst=[ws,density], \n", + " power_arr=power_array, power_unit='w',\n", + " ct_arr=ct_array)\n", + "wt = WindTurbine('AirDensityDependentWT', 112, 84, powerCtFunction=powerCtFunction)\n", + "\n", + "for r in [0.995,1.1,1.225]:\n", + " plt.plot(wt.power(ws, rho=r)/1000, label=f'Air density: {r}')\n", + "plt.ylabel('Power [kW]')\n", + "plt.xlabel('Wind speed [m/s]')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Continous Power/Ct functions\n", + "Finally, the Power can Ct can be calculated using a function which may take multiple input variables." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x25fce9e11f0>" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from py_wake.wind_turbines.power_ct_functions import PowerCtFunction\n", + "\n", + "def density_scaled_power_ct(u, rho=1.225):\n", + " # function to calculate power and ct\n", + " rated_power = 3e6\n", + " density_scale=rho/.95\n", + " return (np.minimum(np.interp(u,ws, p0) * density_scale, rated_power), # density scaled power, limited by rated power\n", + " u*0) #dummy ct\n", + "\n", + "powerCtFunction = PowerCtFunction(\n", + " input_keys=['ws','rho'], \n", + " power_ct_func=density_scaled_power_ct,\n", + " power_unit='w', \n", + " optional_inputs=['rho'], # allowed to be optional as a default value is speficifyed in density_scaled_power_ct\n", + ")\n", + "wt = WindTurbine('AirDensityDependentWT', 112, 84, powerCtFunction=powerCtFunction)\n", + "\n", + "for r in [0.995,1.1,1.5]:\n", + " plt.plot(wt.power(ws, rho=r)/1000, label=f'Air density: {r}')\n", + "plt.ylabel('Power [kW]')\n", + "plt.xlabel('Wind speed [m/s]')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpolation method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PowerCtTabular\n", + "\n", + "`PowerCtTabular` takes a `method` argument which can be \n", + "\n", + "- `linear`: Linear interpolation (default)\n", + "- `pchip`: Piecewise Cubic Hermite Interpolating Polynomial. Smooth interpolation with continous first order derivatives and not overshoots\n", + "- `spline`: Smooth interpolation with continous first and second order derivatives. Closer to original piecewise linear curve, but may have overshoots" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x25fce9df460>" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "wt_lst = [(m, V80(method=m)) for m in ['linear','pchip','spline']]\n", + "ws = np.linspace(14.8,20,1000)\n", + "for n, wt in wt_lst:\n", + " plt.plot(ws, wt.power(ws)/1e3, label=n)\n", + "plt.ylabel('Power [kW]')\n", + "plt.xlabel('Wind speed [m/s]')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { @@ -270,11 +554,47 @@ } ], "source": [ + "# Top-view plot\n", "from py_wake.examples.data.hornsrev1 import Hornsrev1Site\n", "s = IEA37Site(16)\n", "x,y = s.initial_position.T\n", "wts.plot(x,y,type=np.arange(len(x))%len(types))" ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Side-view plot\n", + "import numpy as np\n", + "yaw_lst = np.array([-30, 10, 90])\n", + "wd=0\n", + "wts.plot_yz(np.array([-600,0,600]), wd=wd, types=[0,1,2], yaw=yaw_lst)\n", + "for i, yaw in enumerate(yaw_lst):\n", + " plt.plot([], 'gray', label=\"WT %d yaw: %d deg\" % (i, yaw))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {