Dynamic_Simulation_sm_ib_kundur.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyObaUdnDHYW9G/tHXlWSjfk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shabansatti/Dynamic-Simulation-SMIB-Kundur/blob/main/Dynamic_Simulation_sm_ib_kundur.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "FxnWdV6CwGbq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e90bdae-ed88-41b8-ebb2-d3c0b9c3b6f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/hallvar-h/dynpssimpy\n",
            "  Cloning https://github.com/hallvar-h/dynpssimpy to /tmp/pip-req-build-rzq7tq37\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/hallvar-h/dynpssimpy /tmp/pip-req-build-rzq7tq37\n",
            "  Resolved https://github.com/hallvar-h/dynpssimpy to commit 31f35a9fe26e137c6613bf8ea90d57e6b184b503\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from dynpssimpy==0.2.0) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from dynpssimpy==0.2.0) (1.10.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from dynpssimpy==0.2.0) (3.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dynpssimpy==0.2.0) (1.5.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (4.40.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (8.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (3.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dynpssimpy==0.2.0) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->dynpssimpy==0.2.0) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->dynpssimpy==0.2.0) (1.16.0)\n",
            "Building wheels for collected packages: dynpssimpy\n",
            "  Building wheel for dynpssimpy (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dynpssimpy: filename=dynpssimpy-0.2.0-py3-none-any.whl size=52018 sha256=c8ea23a6184beb5fab4962f2e6ba3eda182dc5f8b94efbe716364bf8bb752657\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-4scmghzy/wheels/a2/3c/3b/d3502fa307d2aad42e905c31d080ac19e3839af0be9fa90140\n",
            "Successfully built dynpssimpy\n",
            "Installing collected packages: dynpssimpy\n",
            "Successfully installed dynpssimpy-0.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/hallvar-h/dynpssimpy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from dynpssimpy import dynamic\n",
        "import dynpssimpy.ps_models.sm_ib_kundur as PS\n",
        "import dynpssimpy.dynamic as dyn\n",
        "import dynpssimpy.solvers as solver\n",
        "from collections import defaultdict\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "TRh141l013Eh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step-1: Loading the Power System Model\n",
        "model=PS.load();\n",
        "\n",
        "# Step-2: Instantiating the PowerSystemModel\n",
        "smib=dyn.PowerSystemModel(model);"
      ],
      "metadata": {
        "id": "pUOGMjAR2L3q"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smib.setup_ready = False;\n",
        "smib.setup();\n",
        "ybus_lf = smib.build_y_bus_lf();\n",
        "print(\"Load Flow Admittance Matrix: \\n\", ybus_lf)\n",
        "print(\"Dimensions Admittance Matrix: \\n\", np.shape(ybus_lf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1yST7guXoEm",
        "outputId": "35febb66-15a6-420b-83eb-eef9ad5c0168"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Load Flow Admittance Matrix: \n",
            " [[0.-1.53846154j 0.+1.53846154j]\n",
            " [0.+1.53846154j 0.-1.53846154j]]\n",
            "Dimensions Admittance Matrix: \n",
            " (2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate\n",
        "smib.power_flow();\n",
        "print (\"Power Flow Ready: \\n\", smib.power_flow_ready)\n",
        "print (\"Power Flow Solution\")\n",
        "print (\"Voltage: \", np.absolute(smib.v0));\n",
        "print (\"Angle: \", np.angle(smib.v0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfRY8iSFab60",
        "outputId": "b7f01dbc-a1f0-480b-b6d9-a4925d8d68f2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Power Flow Ready: \n",
            " True\n",
            "Power Flow Solution\n",
            "Voltage:  [1.    0.995]\n",
            "Angle:  [0.63513303 0.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smib.init_dyn_sim();\n",
        "print (\"Reduced Admittance Matrix: \\n\", smib.y_bus_red_full)\n",
        "print (\"Dimesions of Red Admittance Matrix: \\n\", np.shape(smib.y_bus_red_full))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmC-6zDgdttu",
        "outputId": "b474bb01-b3f5-492e-f4f8-f39f42121b90"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reduced Admittance Matrix: \n",
            " [[0. -5.88628763j 0. +1.53846154j]\n",
            " [0. +1.53846154j 0.-45.01672241j]]\n",
            "Dimesions of Red Admittance Matrix: \n",
            " (2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_step   =   0.001;\n",
        "start_time  =   0;\n",
        "end_time    =   5;\n",
        "result_dict =   defaultdict(list)\n",
        "smib_states =   smib.x_0;\n",
        "fault       =   0;\n",
        "gen_bus_idx = smib.gen['GEN'].bus_idx_red['terminal'][0]\n",
        "\n",
        "print (\"States Descriptor: \\n\", smib.state_desc);\n",
        "print (\"Initial States: \\n\", smib_states)\n",
        "print ('No. of States: ', np.shape(smib_states))\n",
        "print (gen_bus_idx);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uaBu7Dpkf9es",
        "outputId": "8e2664e4-2822-497f-8ea3-ac24f817ad6e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "States Descriptor: \n",
            " [['GOV1/LeadLag' 'x']\n",
            " ['GOV1/TimeConstantLims' 'x']\n",
            " ['AVR1/LeadLag' 'x']\n",
            " ['AVR1/TimeConstantLims' 'x']\n",
            " ['G1' 'speed']\n",
            " ['G1' 'angle']\n",
            " ['G1' 'e_q_t']\n",
            " ['G1' 'e_d_t']\n",
            " ['G1' 'e_q_st']\n",
            " ['G1' 'e_d_st']\n",
            " ['IB' 'speed']\n",
            " ['IB' 'angle']\n",
            " ['IB' 'e_q_t']\n",
            " ['IB' 'e_d_t']\n",
            " ['IB' 'e_q_st']\n",
            " ['IB' 'e_d_st']]\n",
            "Initial States: \n",
            " [ 0.          0.90818182 -0.13572889  2.26214819  0.          1.43948845\n",
            "  0.95356011  0.45433292  0.89289708  0.62624268  0.         -0.15555948\n",
            "  0.99589148 -0.09848995  0.99288005 -0.13446019]\n",
            "No. of States:  (16,)\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t       = start_time;\n",
        "states  = smib_states;\n",
        "\n",
        "while t < end_time:\n",
        "\n",
        "  sol = solver.ModifiedEulerDAE(smib.state_derivatives, smib.solve_algebraic, t, states, end_time, max_step = time_step);\n",
        "  sol.step();\n",
        "  states = sol.y;\n",
        "  t      = sol.t;\n",
        "  if (t>0.1 and t<0.15):\n",
        "    smib.y_bus_red_mod [gen_bus_idx,gen_bus_idx]    = 1e10 * fault;\n",
        "  else:\n",
        "    smib.y_bus_red_mod [gen_bus_idx,gen_bus_idx]    = 0;\n",
        "\n",
        "  result_dict['Global', 't'].append(sol.t)\n",
        "  [result_dict[tuple(desc)].append(state) for desc, state in zip(smib.state_desc, states)]\n"
      ],
      "metadata": {
        "id": "0v26CRL9h-fo"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "state1 = 'speed';\n",
        "state2 = 'angle';\n",
        "\n",
        "plt.plot(result_dict[('Global', 't')], result_dict[('G1', state1)])\n",
        "plt.title(\"Speed\")\n",
        "plt.show()\n",
        "plt.plot(result_dict[('Global', 't')], result_dict[('G1', state2)])\n",
        "plt.title(\"Angle\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "WZDV-cWEphBm",
        "outputId": "09eebc45-c15b-46b0-8263-1c53be7c1d29"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}