WPT_Satellite_BetaAngle.ipynb

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    "colab": {
      "name": "WPT_Satellite.ipynb",
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-CUIzWUlsPfy"
      },
      "source": [
        "#Wireless Power Transmission Satellite Beta Angle Calculations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pgvdpOxYlYu"
      },
      "source": [
        "#Importing necessary libraries\n",
        "import math\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "import random\n",
        "from matplotlib import style"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQR6ds2mx6Pq"
      },
      "source": [
        "##Satellite class to compute Beta Angle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_enm53BMcT-Y"
      },
      "source": [
        "class Satellite:\n",
        "  def __str__(Self):\n",
        "    return 'Enter the necessary parameter of the satellite.\\n Various parameters must be given in degrees '\n",
        "  def __init__(self):\n",
        "    self.Gaama_st = 0\n",
        "    self.Inclination = 0\n",
        "    self.Omega_t = 0\n",
        "    self.Omega_st = 0\n",
        "  def BetaAngle(self):\n",
        "    Beta = math.asin((math.cos(math.radians(self.Gaama_st))*math.sin(math.radians(self.Inclination))*math.sin(math.radians((self.Omega_t-self.Omega_st))))+(math.sin(math.radians(self.Gaama_st))*math.cos(math.radians(self.Inclination))))\n",
        "    if Beta<0:\n",
        "      ans = abs(Beta+1.570796327)*(180/np.pi)\n",
        "      return ans\n",
        "      # print(str(Beta+1.570796327)+' Degrees')\n",
        "      # print(str(ans)+' Radians')\n",
        "    elif Beta>=0:\n",
        "      ans = (Beta)*(180/np.pi)\n",
        "      return ans\n",
        "      # print(str(Beta)+' Degrees')\n",
        "      # print(str(ans)+' Radians')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWgR6VoFcVWm"
      },
      "source": [
        "#Parameters for calculating Beta Angle\n",
        "\n",
        "satellite = Satellite()\n",
        "\n",
        "satellite.Gaama_st = -1.548     #Angle of declination of Sun\n",
        "satellite.Inclination = 21.902 #Angle of Inclinaton of orbit with respect to Equatorial plane of Earth\n",
        "satellite.Omega_t = 4.308      #Right Ascension of Ascending Node (RAAN)\n",
        "satellite.Omega_st = 0         #Right Ascension of Sun"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msMwKEJ5yI8C"
      },
      "source": [
        "##Calculating the positive half-cycle and the negative half-cycle of the orbit  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbJs5sWNcW4G",
        "outputId": "cafe234c-c58d-4ed2-c45c-81932b10e9cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "halfdays = []\n",
        "for i in range(1,366):\n",
        "    if i%13==0:\n",
        "        halfdays.append(i)\n",
        "halfdays.insert(0,1)\n",
        "len(halfdays)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16tAjf4FyuNu"
      },
      "source": [
        "##Creating a list of all the possible Beta Angle values throughout one Earth year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YotYmprAN8Py"
      },
      "source": [
        "Beta_Angles_Degrees = []\n",
        "Days = []\n",
        "for day in range(1,366):\n",
        "    if (day>= halfdays[0] and day< halfdays[1]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif (day>= halfdays[1] and day< halfdays[2]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[2] and day< halfdays[3]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[3] and day< halfdays[4]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[4] and day< halfdays[5]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[5] and day< halfdays[6]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    if (day>= halfdays[6] and day< halfdays[7]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif (day>= halfdays[7] and day< halfdays[8]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[8] and day< halfdays[9]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[10] and day< halfdays[11]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[11] and day< halfdays[12]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[12] and day< halfdays[13]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    if (day>= halfdays[13] and day< halfdays[14]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif (day>= halfdays[14] and day< halfdays[15]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[15] and day< halfdays[16]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[16] and day< halfdays[17]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[17] and day< halfdays[18]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[18] and day< halfdays[19]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    if (day>= halfdays[19] and day< halfdays[20]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif (day>= halfdays[20] and day< halfdays[21]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[21] and day< halfdays[22]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[22] and day< halfdays[23]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[23] and day< halfdays[24]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[24] and day< halfdays[25]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    if (day>= halfdays[25] and day< halfdays[26]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif (day>= halfdays[26] and day< halfdays[27]):\n",
        "        satellite.Gaama_st=-1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n",
        "    elif(day>= halfdays[27] and day<= halfdays[28]):\n",
        "        satellite.Gaama_st=1.548\n",
        "        Days.append(day)\n",
        "        satellite.Omega_st += 13.187\n",
        "        Beta_Angles_Degrees.append(satellite.BetaAngle())\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgMnE6MpcaZO",
        "outputId": "22ee12ea-ca9a-47c4-cbb3-a6b8a62f6890",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(Beta_Angles_Degrees)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "351"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bi43i91wy9V-"
      },
      "source": [
        "##Angle Fluctuations\n",
        "Since Beta Angle lies between 0 degrees to 90 degrees the graphical representation of the same would result in unecessary spikes in the output graph. So, any value fluctuation resulting in angle greater than 90 degrees would result in a low value which on the output graph would represent a low spike. Hence, adjacent values are correlated for optimized output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNzufIoNccNu"
      },
      "source": [
        "for i in range(1,len(Beta_Angles_Degrees)):\n",
        "    if Beta_Angles_Degrees[i]*2 < Beta_Angles_Degrees[i-1]:\n",
        "        Beta_Angles_Degrees[i]=90+Beta_Angles_Degrees[i]\n",
        "    else: continue"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FU-rlNjscd82",
        "outputId": "62a0d6f8-b1b2-48a2-de7b-713eedd85d2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        }
      },
      "source": [
        "#Beta angle variation with respect to Right Ascension of Sun\n",
        "plt.figure(figsize=(20,10))\n",
        "style.use('fivethirtyeight')\n",
        "plt.plot(Days,Beta_Angles_Degrees, linestyle='-', color='b')\n",
        "plt.xlim(-10,380)\n",
        "plt.ylim(-10,200)\n",
        "# plt.xticks(RAAN_Angles)\n",
        "plt.xlabel('Day')\n",
        "plt.ylabel('Beta Angle')\n",
        "plt.legend() \n",
        "#plt.savefig('BetaAngle_at_Various_RAS_FullYear.png', transparent = False,dpi=300, bbox_inches='tight',facecolor = 'w')         #Save the Plot\n",
        "plt.show()\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdiBQm--cka-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}