mikofski/trina_gen_2diode_coeffs.ipynb

## trina_gen_2diode_coeffs.ipynb
{
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/mikofski/eec389cf0bb004b2c8f94a3615ca6ac5/trina_gen_2diode_coeffs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "bb18f108-a437-436e-90d4-75f4a3db098e",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "bb18f108-a437-436e-90d4-75f4a3db098e",
        "outputId": "22df77d7-afd1-407e-b8c8-a6d2fdd5e3d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pvmismatch in /usr/local/lib/python3.10/dist-packages (4.1)\n",
            "Collecting pvlib\n",
            "  Downloading pvlib-0.10.4-py3-none-any.whl (29.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.5/29.5 MB\u001b[0m \u001b[31m42.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from pvmismatch) (1.25.2)\n",
            "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from pvmismatch) (3.7.1)\n",
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            "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from pvlib) (2023.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from pvlib) (2.31.0)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pvmismatch) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pvmismatch) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pvmismatch) (2.8.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->pvlib) (2024.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->pvlib) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->pvlib) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->pvlib) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->pvlib) (2024.2.2)\n",
            "Installing collected packages: pvlib\n",
            "Successfully installed pvlib-0.10.4\n"
          ]
        }
      ],
      "source": [
        "!pip install pvmismatch pvlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "7df4c2d2-b8cf-4719-8e60-197383af3314",
      "metadata": {
        "id": "7df4c2d2-b8cf-4719-8e60-197383af3314"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from pvmismatch.contrib.gen_coeffs import gen_two_diode\n",
        "from pvmismatch import *\n",
        "\n",
        "def last_guess(sol):\n",
        "    isat1 = np.exp(sol.x[0])\n",
        "    isat2 = np.exp(sol.x[1])\n",
        "    rs = sol.x[2] ** 2.0\n",
        "    rsh = sol.x[3] ** 2.0\n",
        "    return isat1, isat2, rs, rsh"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "223e404c-a51a-4f8f-adad-641265f255a5",
      "metadata": {
        "id": "223e404c-a51a-4f8f-adad-641265f255a5"
      },
      "source": [
        "# example generation of 2-diode coefficients\n",
        "[Trina Vertex_DEG19RC.20_EN_2022A](https://static.trinasolar.com/sites/default/files/DT-M-0034A_Datasheet_Vertex_DEG19RC.20_EN_2022A.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "5313d170-2dc5-4b72-8ffa-fde8f17c9e3b",
      "metadata": {
        "id": "5313d170-2dc5-4b72-8ffa-fde8f17c9e3b"
      },
      "outputs": [],
      "source": [
        "electrical_data = {\n",
        "    550: {\"vmp\": 37.2, \"imp\": 14.78, \"voc\": 44.8, \"isc\": 15.76}}\n",
        "beta_voc = -0.0025  # per degC\n",
        "alpha_isc = 0.0004  # per degC\n",
        "nseries = 66\n",
        "nparallel = 2\n",
        "tc = 25.0  # degC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "b6b5430d-4a6e-44f2-bd84-493e8168e4a6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6b5430d-4a6e-44f2-bd84-493e8168e4a6",
        "outputId": "d8023d43-5ae4-426c-f2c4-90ccd1a25368"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " message: The solution converged.\n",
              " success: True\n",
              "  status: 1\n",
              "     fun: [ 5.430e-10 -6.277e-11 -4.501e-10  1.139e-14]\n",
              "       x: [-2.446e+01 -1.350e+01  6.605e-02  3.028e+00]\n",
              "    nfev: 8\n",
              "    njev: 1\n",
              "    fjac: [[-7.774e-01 -4.070e-02 -6.277e-01  1.692e-06]\n",
              "           [ 6.266e-01 -1.365e-01 -7.673e-01 -5.690e-06]\n",
              "           [ 5.448e-02  9.898e-01 -1.316e-01  5.477e-05]\n",
              "           [-1.897e-06  5.492e-05 -3.907e-06 -1.000e+00]]\n",
              "       r: [ 9.096e+00  1.509e+00  1.440e+02 -5.465e-02  6.668e-01\n",
              "            1.676e+02 -2.647e-03  1.486e+01  3.546e-02  3.714e-06]\n",
              "     qtf: [ 1.547e-09 -1.096e-08 -6.425e-10  1.440e-13]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "isc, voc = electrical_data[550]['isc'], electrical_data[550]['voc']\n",
        "imp, vmp = electrical_data[550]['imp'], electrical_data[550]['vmp']\n",
        "x, sol = gen_two_diode(isc, voc, imp, vmp, nseries, nparallel, tc)\n",
        "sol"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "71db7c5b-2977-4166-840a-598c68f50ce5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "f8ab5c9f-05e1-4b1f-ce6d-3d923fe3b6f5"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.3722544230539216e-11,\n",
              " 1.3665379526271328e-06,\n",
              " 0.004362363709123517,\n",
              " 9.168352828127766)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "387c4603-8ee0-449e-8346-06ee1e668636",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "387c4603-8ee0-449e-8346-06ee1e668636",
        "outputId": "79c39e6d-870a-417b-c74a-aaddfafd93af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.3722544230539216e-11,\n",
              " 1.3665379526271328e-06,\n",
              " 0.004362363709123517,\n",
              " 9.168352828127766)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "last_guess(sol)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "e2036134-161c-4cff-868d-2ade1714c30a",
      "metadata": {
        "id": "e2036134-161c-4cff-868d-2ade1714c30a"
      },
      "outputs": [],
      "source": [
        "pvc = pvcell.PVcell(\n",
        "    Isat1_T0=x[0],\n",
        "    Isat2_T0=x[1],\n",
        "    Rs=x[2],\n",
        "    Rsh=x[3],\n",
        "    Isc0_T0=isc/nparallel,\n",
        "    alpha_Isc=alpha_isc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "58d0518e-8567-4a9e-a5d0-eeeded705a1b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58d0518e-8567-4a9e-a5d0-eeeded705a1b",
        "outputId": "0c099a5d-036b-434e-83f3-9bb56c84f74a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44.816799849648454"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "pvc.Voc*nseries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "8807f17e-e00c-44c9-a9c9-aaef8d06e556",
      "metadata": {
        "id": "8807f17e-e00c-44c9-a9c9-aaef8d06e556"
      },
      "outputs": [],
      "source": [
        "mpp = np.argmax(pvc.Pcell)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "afe0b15a-9f31-4bd8-a83c-9df45b6e744e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afe0b15a-9f31-4bd8-a83c-9df45b6e744e",
        "outputId": "ae9938bb-43ad-4b74-ce7f-42aa4c4a36b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37.18592977422984"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "pvc.Vcell[mpp][0]*nseries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "3089ef49-cee5-44bd-9568-579a6ef5a004",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3089ef49-cee5-44bd-9568-579a6ef5a004",
        "outputId": "41e2e261-d6bd-4607-a423-ca7a2e2fbe39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.78556291473152"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "pvc.Icell[mpp][0]*nparallel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "7a642e8b-d09d-457f-919b-58e1122f2d9c",
      "metadata": {
        "id": "7a642e8b-d09d-457f-919b-58e1122f2d9c"
      },
      "outputs": [],
      "source": [
        "halfcell132 = pvmodule.crosstied_cellpos_pat([22, 22, 22], 2, partial=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "ba843200-081c-44de-b806-66f6836ecad4",
      "metadata": {
        "id": "ba843200-081c-44de-b806-66f6836ecad4"
      },
      "outputs": [],
      "source": [
        "pvm = pvmodule.PVmodule(cell_pos=halfcell132, pvcells=[pvc]*132)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "08c3d592-8b08-4edc-9ca8-bdd7daaa8f5e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "08c3d592-8b08-4edc-9ca8-bdd7daaa8f5e",
        "outputId": "b8a302f4-6d42-4783-846f-f99e431dea7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ebb5c8f4bb0>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# make some comparison plots\n",
        "pvm.plotMod()  # plot the pvmm module\n",
        "plt.tight_layout()\n",
        "\n",
        "f = plt.gcf()\n",
        "ax = f.axes\n",
        "ax[0].set_ylim([0, 20])\n",
        "ax[0].plot(0, isc, 'or')\n",
        "ax[0].plot(voc, 0, 'or')\n",
        "ax[0].plot(vmp, imp, 'or')\n",
        "\n",
        "ax[0].plot(0, pvm.Isc.mean()*nparallel, 'xk')\n",
        "ax[0].plot(pvm.Voc.sum()/nparallel, 0, 'xk')\n",
        "\n",
        "mpp = np.argmax(pvm.Pmod)\n",
        "ax[0].plot(pvm.Vmod[mpp], pvm.Imod[mpp], 'xk')\n",
        "ax[1].plot(vmp, imp*vmp, 'or')\n",
        "ax[1].plot(pvm.Vmod[mpp], pvm.Pmod[mpp], 'xk')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "d1bb0221-1aee-4132-a202-3ee6a67deb3d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1bb0221-1aee-4132-a202-3ee6a67deb3d",
        "outputId": "5aff99c7-82f1-4fe1-ac69-ac7c530b898c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(37.22876225039524, 14.765733698218524)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "mpp = np.argmax(pvm.Pmod)\n",
        "pvm.Vmod[mpp], pvm.Imod[mpp]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "fffb8429-ad99-47b4-a83c-3f20d5cc1995",
      "metadata": {
        "id": "fffb8429-ad99-47b4-a83c-3f20d5cc1995"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "8063a023-5dc4-4c5a-aa2a-5e0233902431",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8063a023-5dc4-4c5a-aa2a-5e0233902431",
        "outputId": "18455b5f-c725-4c77-c000-b221976747f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(132,)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "pvm.Vcell[:, mpp].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "8ff0a321-1f6d-41c2-9468-67e88c93c032",
      "metadata": {
        "id": "8ff0a321-1f6d-41c2-9468-67e88c93c032"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.11.4"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}