noj.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# NOJ WakeModel"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "[Try this yourself](https://colab.research.google.com/github/DTUWindEnergy/PyWake/blob/master/docs/notebooks/noj.ipynb) (requires google account)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 0,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Install PyWake if needed\n",
                "try:\n",
                "    import py_wake\n",
                "except ModuleNotFoundError:\n",
                "    !pip install py_wake\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "\n",
                "from py_wake.examples.data.hornsrev1 import V80, Hornsrev1Site, wt9_x, wt9_y\n",
                "from py_wake.wake_models.noj import NOJ\n",
                "windTurbines = V80()\n",
                "wake_model = NOJ(windTurbines, k=0.1)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "The `NOJ` is a subclass of the general `WakeModel` class, see documentation [here](https://topfarm.pages.windenergy.dtu.dk/PyWake/wake_models/WakeModel.html)\n",
                "\n",
                "It implements the wake model of Niels Otto Jensen, described in \"A note on wind generator interaction.\" (1983)\n",
                "\n",
                "The implementation of `WakeModel` is highly vectorized and therefore suffixes are used to indicate the dimension of variables. The suffixes used in this context are:\n",
                "\n",
                "- i: turbines ordered by id\n",
                "- k: wind speeds\n",
                "- l: wind directions\n",
                "\n",
                "This means that `WS_ilk[0,1,2]` holds the wind speed at the first turbine for the second wind direction and third wind speed\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "`WakeModel` contains a method, [`calc_wake`](https://topfarm.pages.windenergy.dtu.dk/PyWake/wake_models/WakeModel.html#py_wake.wake_model.WakeModel.calc_wake), to calculate the effective wind speed, turbulence intensity (not implemented yet), power and thrust coefficient.\n",
                "\n",
                "Let us try to calculate the effective wind speed for two V80 turbines separated by 200m in 10m/s and wind direction parallel to a line between the two turbines"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Turbine 0\n",
                        "Effective wind speed 10.00m/s\n",
                        "Power production 1341000.00W\n",
                        "Trust coefficient 0.793000\n",
                        "\n",
                        "Turbine 1\n",
                        "Effective wind speed 7.58m/s\n",
                        "Power production 596326.74W\n",
                        "Trust coefficient 0.805578\n",
                        "\n"
                    ]
                }
            ],
            "source": [
                "WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk = wake_model.calc_wake(\n",
                "    WS_ilk=np.array([[[10]],[[10]]]), # 10m/s at both turbines\n",
                "    TI_ilk=np.array([[[.1]],[[.1]]]), # turbulence intensity (not used)\n",
                "    dw_iil=np.array([[0,200],[-200,0]])[:,:,np.newaxis], # 200m down stream separation\n",
                "    hcw_iil=np.array([[0,0],[0,0]])[:,:,np.newaxis], # wt aligned with wind -> zero cross wind distance\n",
                "    dh_iil=np.array([[0,0],[0,0]])[:,:,np.newaxis], # no hub height difference\n",
                "    dw_order_indices_dl=np.array([[0,1]]), # down stream order of turbines\n",
                "    types_i=[0,0]) # both are turbine type 0\n",
                "\n",
                "for i in [0,1]:\n",
                "    print ('Turbine', i)\n",
                "    print ('Effective wind speed %.2fm/s'%WS_eff_ilk[i,0,0])\n",
                "    print ('Power production %.2fW'%power_ilk[i,0,0])\n",
                "    print ('Trust coefficient %f'%ct_ilk[i,0,0])\n",
                "    print()\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "To calculate this, `calc_wake`, uses two wake-model specific methods, `calc_deficit` and `calc_effective_WS`.\n",
                "\n",
                "`calc_deficit` calculates the deficit:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 48,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "2.422344326030697\n"
                    ]
                }
            ],
            "source": [
                "deficit = wake_model.calc_deficit(\n",
                "    WS_lk=np.array([[10]]), # wind speed at current turbine\n",
                "    D_src_l=np.array([80]), # diameter of current turbine\n",
                "    D_dst_jl=np.array([[80]]), # diameter of downstream turbine(s)\n",
                "    dw_jl=np.array([[200]]), # down wind distance\n",
                "    cw_jl=np.array([[0]]), # cross wind distance (both horizontal and vertical)\n",
                "    ct_lk=np.array([[0.793000]])) # thrust coefficient\n",
                "print (deficit[0,0,0])\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 49,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<matplotlib.lines.Line2D at 0x1a291871860>]"
                        ]
                    },
                    "execution_count": 49,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
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\n",
                        "text/plain": [
                            "<Figure size 432x288 with 2 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "x = np.arange(-300,301,1.)\n",
                "y = np.arange(-100,1001,1)\n",
                "X,Y = np.meshgrid(x,y)\n",
                "down_stream = Y>0\n",
                "deficit_map=np.zeros_like(X)\n",
                "\n",
                "deficit_map[down_stream] = wake_model.calc_deficit(\n",
                "    WS_lk=np.array([[10]]), # wind speed at current turbine\n",
                "    D_src_l=np.array([80]), # diameter of current turbine\n",
                "    D_dst_jl=np.zeros_like(X)[down_stream][:,np.newaxis], # diameter of downstream turbine(s)\n",
                "    dw_jl=Y[down_stream][:,np.newaxis], # down wind distance\n",
                "    cw_jl=np.abs(X[down_stream])[:,np.newaxis], # cross wind distance (both horizontal and vertical)\n",
                "    ct_lk=np.array([[0.793000]]))[:,0,0] # thrust coefficient\n",
                "\n",
                "c = plt.contourf(Y,X,deficit_map,100)\n",
                "plt.colorbar(c)\n",
                "plt.plot([0,0],[-40,40],'r',lw=3)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "while `calc_effective_WS` calculates the effective wind speed by subtracting the deficits from all upstream turbines from the local wind speed. For `NOJ` it subtracts the square root of the sum of squared deficits"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 50,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array([[7.57765567]])"
                        ]
                    },
                    "execution_count": 50,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "wake_model.calc_effective_WS(\n",
                "    WS_lk=np.array([[10]]), \n",
                "    deficit_jlk=deficit)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Finally, `WakeModel`, contains the method `wake_map` to find the effective wind speed at arbitrary positions"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array([[[7.57765567]]])"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "#calculate the wake 200m down stream of a V80\n",
                "\n",
                "wake_model.wake_map(\n",
                "    WS_ilk=np.array([[[10]]]), # wind speed at turbine\n",
                "    WS_eff_ilk=np.array([[[8.92340252]]]), \n",
                "    dw_ijl=np.array([[[200]]]), # one point 500m down stream \n",
                "    hcw_ijl=np.array([[[0]]]), # 0m cross wind distance \n",
                "    dh_ijl=np.array([[[0]]]), # at hub height\n",
                "    ct_ilk=np.array([[[0.793000]]]), \n",
                "    types_i=[0], \n",
                "    WS_jlk=np.array([[[10]]]), # local wind speed at point\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "For standard purposes, however, we do not call these methods manually. Instead we use the functions in `AEPCalculator`"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "**Calcculate AEP**"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "AEP pr turbine: [9.00648938 9.17948543]\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.legend.Legend at 0x1a2f6a05cf8>"
                        ]
                    },
                    "execution_count": 6,
                    "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"
                },
                {
                    "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.aep_calculator import AEPCalculator\n",
                "\n",
                "site = Hornsrev1Site()\n",
                "aep_calc = AEPCalculator(site, windTurbines, wake_model)\n",
                "\n",
                "aep_gwh_ilk = aep_calc.calculate_AEP(x_i=[0,0], y_i=[0,-200])\n",
                "aep_gwh_noloss_ilk = aep_calc.calculate_AEP_no_wake_loss(x_i=[0,0], y_i=[0,-200])\n",
                "\n",
                "\n",
                "# AEP pr turbine\n",
                "print ('AEP pr turbine:', aep_gwh_ilk.sum((1,2)))\n",
                "\n",
                "# AEP pr wind direction\n",
                "plt.plot(aep_gwh_noloss_ilk[:,:,7].sum(0), label='Without wake loss')\n",
                "plt.plot(aep_gwh_ilk[:,:,7].sum(0), label='With wake loss')\n",
                "plt.title('Wind speed: 10m/s')\n",
                "plt.xlabel('Wind direction [deg]')\n",
                "plt.ylabel('AEP [GWh]')\n",
                "plt.legend()\n",
                "\n",
                "# AEP pr wind speed\n",
                "plt.figure()\n",
                "plt.plot(site.default_ws, aep_gwh_noloss_ilk[:,0].sum(0), label='Without wake loss')\n",
                "plt.plot(site.default_ws, aep_gwh_ilk[:,0].sum(0), label='With wake loss')\n",
                "plt.title('Wind direction: 0deg')\n",
                "plt.xlabel('Wind speed [deg]')\n",
                "plt.ylabel('AEP w[GWh]')\n",
                "plt.legend()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "**Wake map**"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 432x288 with 2 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "import matplotlib.pyplot as plt\n",
                "x,y = wt9_x, wt9_y\n",
                "aep_calc.plot_wake_map(wt_x=x, wt_y=y, wd=[0], ws=[10])\n",
                "windTurbines.plot(x, y)\n",
                "plt.show()\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "**Effective wind speed, power and thrust coefficient**"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Effective wind speed: wt0: 10.000000\twt1: 7.577656\n",
                        "Power: wt0: 1341000W\twt1: 596326W\n",
                        "Thrust coefficient: wt0: 0W\twt1: 0W\n",
                        "Probability of ws=10m/s and wd=0deg: 0.000103\n"
                    ]
                }
            ],
            "source": [
                "# wind from 0 deg(index=0) and 10m/s (index=7)\n",
                "\n",
                "aep_calc.calculate_AEP(x_i=[0,0], y_i=[0,-200])\n",
                "print (\"Effective wind speed: wt0: %f\\twt1: %f\"%tuple(aep_calc.WS_eff_ilk[:,0,7])) \n",
                "print (\"Power: wt0: %dW\\twt1: %dW\"%tuple(aep_calc.power_ilk[:,0,7])) \n",
                "print (\"Thrust coefficient: wt0: %dW\\twt1: %dW\"%tuple(aep_calc.ct_ilk[:,0,7])) \n",
                "print (\"Probability of ws=10m/s and wd=0deg: %f\"%aep_calc.P_lk[0,7])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "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": "3.6.7"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}