├── ANN_Model_Code.ipynb ├── Data.xlsx ├── Features.xlsx ├── MicroGridModel.slx ├── Project.pdf └── README.md /ANN_Model_Code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "ANN Model Code.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [], 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | }, 15 | "accelerator": "GPU" 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "markdown", 20 | "metadata": { 21 | "id": "view-in-github", 22 | "colab_type": "text" 23 | }, 24 | "source": [ 25 | "\"Open" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "metadata": { 31 | "id": "vh_2PFdoc83n", 32 | "colab_type": "code", 33 | "colab": { 34 | "base_uri": "https://localhost:8080/", 35 | "height": 34 36 | }, 37 | "outputId": "3c535a50-5853-468d-b9f9-5a1d148a13ea" 38 | }, 39 | "source": [ 40 | "import numpy as np\n", 41 | "import pandas as pd\n", 42 | "from keras.models import Sequential\n", 43 | "from keras.layers import Dense\n", 44 | "from keras.layers import Dropout\n", 45 | "from keras.wrappers.scikit_learn import KerasClassifier\n", 46 | "from sklearn.model_selection import cross_val_score\n", 47 | "from sklearn.preprocessing import LabelEncoder\n", 48 | "from sklearn.model_selection import StratifiedKFold\n", 49 | "from sklearn.preprocessing import StandardScaler\n", 50 | "from sklearn.pipeline import Pipeline" 51 | ], 52 | "execution_count": null, 53 | "outputs": [ 54 | { 55 | "output_type": "stream", 56 | "text": [ 57 | "Using TensorFlow backend.\n" 58 | ], 59 | "name": "stderr" 60 | } 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "metadata": { 66 | "id": "p4mPCB1wwZQC", 67 | "colab_type": "code", 68 | "colab": {} 69 | }, 70 | "source": [ 71 | "seed=7\n", 72 | "np.random.seed(seed)" 73 | ], 74 | "execution_count": null, 75 | "outputs": [] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "metadata": { 80 | "id": "dOR-kUC5dPB7", 81 | "colab_type": "code", 82 | "colab": { 83 | "base_uri": "https://localhost:8080/", 84 | "height": 360 85 | }, 86 | "outputId": "eb51bf24-927c-49ee-851f-91541dde974c" 87 | }, 88 | "source": [ 89 | "df=pd.read_excel('Data.xlsx')\n", 90 | "df1=df.values\n", 91 | "df" 92 | ], 93 | "execution_count": null, 94 | "outputs": [ 95 | { 96 | "output_type": "error", 97 | "ename": "FileNotFoundError", 98 | "evalue": "ignored", 99 | "traceback": [ 100 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 101 | "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", 102 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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116\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpeek\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34mb\"PK\\x03\\x04\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# a ZIP file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 109 | "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Data.xlsx'" 110 | ] 111 | } 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "metadata": { 117 | "id": "rOOARm0VdcBp", 118 | "colab_type": "code", 119 | "colab": {} 120 | }, 121 | "source": [ 122 | "po=pd.DataFrame(columns=['current','load','result','Time'])" 123 | ], 124 | "execution_count": null, 125 | "outputs": [] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "metadata": { 130 | "id": "fbVDvAuRdjpn", 131 | "colab_type": "code", 132 | "colab": {} 133 | }, 134 | "source": [ 135 | "for i in range(23):\n", 136 | " po=po.append({'current':df.at[i,\"Current 1\"],'load':df.at[i,\"P_L 1\"],'result':1,'Time':df.at[i,\"Time\"]},ignore_index=True)" 137 | ], 138 | "execution_count": null, 139 | "outputs": [] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "metadata": { 144 | "id": "oGNxLOtXdlhY", 145 | "colab_type": "code", 146 | "colab": { 147 | "base_uri": "https://localhost:8080/", 148 | "height": 470 149 | }, 150 | "outputId": "13485257-89c0-4378-e9c9-c63977b1cff6" 151 | }, 152 | "source": [ 153 | "po" 154 | ], 155 | "execution_count": null, 156 | "outputs": [ 157 | { 158 | "output_type": "execute_result", 159 | "data": { 160 | "text/html": [ 161 | "
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7696 rows × 3137 columns

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" 1275 | ], 1276 | "text/plain": [ 1277 | " 0 1 2 3 4 ... 3132 3133 3134 3135 label\n", 1278 | "image ... \n", 1279 | "train0.jpg 165 166 174 174 175 ... 69 79 90 78 0\n", 1280 | "train1.jpg 27 44 61 78 96 ... 33 13 2 1 1\n", 1281 | "train2.jpg 0 0 0 0 0 ... 0 0 0 0 1\n", 1282 | "train3.jpg 197 210 204 199 206 ... 199 196 196 197 2\n", 1283 | "train4.jpg 128 119 133 115 109 ... 132 134 131 123 1\n", 1284 | "... ... ... ... ... ... ... ... ... ... ... ...\n", 1285 | "train7691.jpg 192 189 187 188 193 ... 177 180 181 181 4\n", 1286 | "train7692.jpg 0 0 0 0 0 ... 0 0 0 0 3\n", 1287 | "train7693.jpg 96 97 100 105 110 ... 188 189 188 170 4\n", 1288 | "train7694.jpg 116 98 142 158 168 ... 151 161 170 154 5\n", 1289 | "train7695.jpg 0 0 0 1 2 ... 1 1 0 0 1\n", 1290 | "\n", 1291 | "[7696 rows x 3137 columns]" 1292 | ] 1293 | }, 1294 | "metadata": { 1295 | "tags": [] 1296 | }, 1297 | "execution_count": 6 1298 | } 1299 | ] 1300 | }, 1301 | { 1302 | "cell_type": "code", 1303 | "metadata": { 1304 | "id": "mI2aTsIydoJ7", 1305 | "colab_type": "code", 1306 | "colab": {} 1307 | }, 1308 | "source": [ 1309 | "for i in range(23):\n", 1310 | " po=po.append({'current':df.at[i,\"Current 2\"],'load':df.at[i,\"P_L 2\"],'result':2,'Time':df.at[i,\"Time\"]},ignore_index=True)" 1311 | ], 1312 | "execution_count": null, 1313 | "outputs": [] 1314 | }, 1315 | { 1316 | "cell_type": "code", 1317 | "metadata": { 1318 | "id": "6UzQk6EOdq3Q", 1319 | "colab_type": "code", 1320 | "colab": {} 1321 | }, 1322 | "source": [ 1323 | "for i in range(23):\n", 1324 | " po=po.append({'current':df.at[i,\"Current 3\"],'load':df.at[i,\"P_L 3\"],'result':3,'Time':df.at[i,\"Time\"]},ignore_index=True)" 1325 | ], 1326 | "execution_count": null, 1327 | "outputs": [] 1328 | }, 1329 | { 1330 | "cell_type": "code", 1331 | "metadata": { 1332 | "id": "DjyG5rm2dz2m", 1333 | "colab_type": "code", 1334 | "colab": { 1335 | "base_uri": "https://localhost:8080/", 1336 | "height": 34 1337 | }, 1338 | "outputId": "4ba00cec-1721-4da0-e0f4-09f6e2a0878a" 1339 | }, 1340 | "source": [ 1341 | "for i in range(23):\n", 1342 | " po=po.append({'current':df.at[i,\"Current Ideal\"],'load':df.at[i,\"P_L Ideal\"],'result':0,'Time':df.at[i,\"Time\"]},ignore_index=True)" 1343 | ], 1344 | "execution_count": null, 1345 | "outputs": [ 1346 | { 1347 | "output_type": "stream", 1348 | "text": [ 1349 | "Found 8490 images belonging to 7 classes.\n" 1350 | ], 1351 | "name": "stdout" 1352 | } 1353 | ] 1354 | }, 1355 | { 1356 | "cell_type": "code", 1357 | "metadata": { 1358 | "id": "r-5cNtPCd2YG", 1359 | "colab_type": "code", 1360 | "colab": { 1361 | "base_uri": "https://localhost:8080/", 1362 | "height": 122 1363 | }, 1364 | "outputId": "8ad1e5ba-02c1-4bb2-b69e-93516aaabf3a" 1365 | }, 1366 | "source": [ 1367 | "po" 1368 | ], 1369 | "execution_count": null, 1370 | "outputs": [ 1371 | { 1372 | "output_type": "stream", 1373 | "text": [ 1374 | "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", 1375 | "Instructions for updating:\n", 1376 | "If using Keras pass *_constraint arguments to layers.\n", 1377 | "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf.h5\n", 1378 | "17227776/17225924 [==============================] - 3s 0us/step\n" 1379 | ], 1380 | "name": "stdout" 1381 | } 1382 | ] 1383 | }, 1384 | { 1385 | "cell_type": "code", 1386 | "metadata": { 1387 | "id": "t00qelT1d6lX", 1388 | "colab_type": "code", 1389 | "colab": {} 1390 | }, 1391 | "source": [ 1392 | "po = pd.concat([po,pd.get_dummies(po['Time'], prefix='Time',dummy_na=True)],axis=1).drop(['Time'],axis=1)\n" 1393 | ], 1394 | "execution_count": null, 1395 | "outputs": [] 1396 | }, 1397 | { 1398 | "cell_type": "code", 1399 | "metadata": { 1400 | "id": "GtP2JRm5eBDI", 1401 | "colab_type": "code", 1402 | "colab": {} 1403 | }, 1404 | "source": [ 1405 | "po" 1406 | ], 1407 | "execution_count": null, 1408 | "outputs": [] 1409 | }, 1410 | { 1411 | "cell_type": "code", 1412 | "metadata": { 1413 | "id": "JU9SttS7eC1y", 1414 | "colab_type": "code", 1415 | "colab": {} 1416 | }, 1417 | "source": [ 1418 | "poo=po.values" 1419 | ], 1420 | "execution_count": null, 1421 | "outputs": [] 1422 | }, 1423 | { 1424 | "cell_type": "code", 1425 | "metadata": { 1426 | "id": "6YoYMY0heEkG", 1427 | "colab_type": "code", 1428 | "colab": {} 1429 | }, 1430 | "source": [ 1431 | "yy=poo[:,2]\n", 1432 | "po.drop(['result'],axis=\"columns\",inplace=True)" 1433 | ], 1434 | "execution_count": null, 1435 | "outputs": [] 1436 | }, 1437 | { 1438 | "cell_type": "code", 1439 | "metadata": { 1440 | "id": "xQnkBOb_eGou", 1441 | "colab_type": "code", 1442 | "colab": { 1443 | "base_uri": "https://localhost:8080/", 1444 | "height": 360 1445 | }, 1446 | "outputId": "788eb09f-38c6-4068-e3a0-bcf76239f1ea" 1447 | }, 1448 | "source": [ 1449 | "XX=poo[:,:]\n" 1450 | ], 1451 | "execution_count": null, 1452 | "outputs": [ 1453 | { 1454 | "output_type": "stream", 1455 | "text": [ 1456 | "Epoch 1/30\n", 1457 | "119/770 [===>..........................] - ETA: 42:32 - loss: 2.5548 - acc: 0.3437" 1458 | ], 1459 | "name": "stdout" 1460 | }, 1461 | { 1462 | "output_type": "error", 1463 | "ename": "KeyboardInterrupt", 1464 | "evalue": "ignored", 1465 | "traceback": [ 1466 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 1467 | "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", 1468 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 1469 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1295\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m 1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1298\u001b[0m def evaluate_generator(self,\n", 1470 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtarget_steps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0mbatch_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_next_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbatch_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_dataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 1471 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36m_get_next_batch\u001b[0;34m(generator)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0;34m\"\"\"Retrieves the next batch of input data.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 1475 | "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 1476 | "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 1477 | "\u001b[0;31mKeyboardInterrupt\u001b[0m: " 1478 | ] 1479 | } 1480 | ] 1481 | }, 1482 | { 1483 | "cell_type": "code", 1484 | "metadata": { 1485 | "id": "LP99lYcEeIhq", 1486 | "colab_type": "code", 1487 | "colab": {} 1488 | }, 1489 | "source": [ 1490 | "def neural_net():\n", 1491 | " model = Sequential()\n", 1492 | " model.add(Dense(16, input_dim=27, kernel_initializer='normal', activation='relu'))\n", 1493 | " model.add(Dropout(0.2))\n", 1494 | " model.add(Dense(8, kernel_initializer='normal', activation='relu'))\n", 1495 | " model.add(Dense(4, kernel_initializer='normal',activation='softmax'))\n", 1496 | " model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", 1497 | " return model" 1498 | ], 1499 | "execution_count": null, 1500 | "outputs": [] 1501 | }, 1502 | { 1503 | "cell_type": "code", 1504 | "metadata": { 1505 | "id": "hZb562n-yJzJ", 1506 | "colab_type": "code", 1507 | "colab": {} 1508 | }, 1509 | "source": [ 1510 | "from keras.utils import np_utils" 1511 | ], 1512 | "execution_count": null, 1513 | "outputs": [] 1514 | }, 1515 | { 1516 | "cell_type": "code", 1517 | "metadata": { 1518 | "id": "aVL4rZZDyeuy", 1519 | "colab_type": "code", 1520 | "colab": {} 1521 | }, 1522 | "source": [ 1523 | "encoder = LabelEncoder()\n", 1524 | "encoder.fit(yy)\n", 1525 | "encoded_Y = encoder.transform(yy)\n", 1526 | "dummy_y = np_utils.to_categorical(encoded_Y)" 1527 | ], 1528 | "execution_count": null, 1529 | "outputs": [] 1530 | }, 1531 | { 1532 | "cell_type": "code", 1533 | "metadata": { 1534 | "id": "S9LaHICzygN1", 1535 | "colab_type": "code", 1536 | "colab": {} 1537 | }, 1538 | "source": [ 1539 | "dummy_y" 1540 | ], 1541 | "execution_count": null, 1542 | "outputs": [] 1543 | }, 1544 | { 1545 | "cell_type": "code", 1546 | "metadata": { 1547 | "id": "i0oRbw7uyh8N", 1548 | "colab_type": "code", 1549 | "colab": {} 1550 | }, 1551 | "source": [ 1552 | "dummy_XX=XX" 1553 | ], 1554 | "execution_count": null, 1555 | "outputs": [] 1556 | }, 1557 | { 1558 | "cell_type": "code", 1559 | "metadata": { 1560 | "id": "tLdxBr17ylks", 1561 | "colab_type": "code", 1562 | "colab": {} 1563 | }, 1564 | "source": [ 1565 | "scaler=StandardScaler()" 1566 | ], 1567 | "execution_count": null, 1568 | "outputs": [] 1569 | }, 1570 | { 1571 | "cell_type": "code", 1572 | "metadata": { 1573 | "id": "XLkUP8O-ypti", 1574 | "colab_type": "code", 1575 | "colab": {} 1576 | }, 1577 | "source": [ 1578 | "dummy_XX=scaler.fit_transform(dummy_XX)" 1579 | ], 1580 | "execution_count": null, 1581 | "outputs": [] 1582 | }, 1583 | { 1584 | "cell_type": "code", 1585 | "metadata": { 1586 | "id": "EQCCA6QkyrvO", 1587 | "colab_type": "code", 1588 | "colab": {} 1589 | }, 1590 | "source": [ 1591 | "dummy_XX" 1592 | ], 1593 | "execution_count": null, 1594 | "outputs": [] 1595 | }, 1596 | { 1597 | "cell_type": "code", 1598 | "metadata": { 1599 | "id": "w-snfrWbytzP", 1600 | "colab_type": "code", 1601 | "colab": {} 1602 | }, 1603 | "source": [ 1604 | "mm=neural_net()\n", 1605 | "history=mm.fit(XX,dummy_y,epochs=500)" 1606 | ], 1607 | "execution_count": null, 1608 | "outputs": [] 1609 | } 1610 | ] 1611 | } -------------------------------------------------------------------------------- /Data.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/Data.xlsx -------------------------------------------------------------------------------- /Features.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/Features.xlsx -------------------------------------------------------------------------------- /MicroGridModel.slx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/MicroGridModel.slx -------------------------------------------------------------------------------- /Project.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AgHarsh/Fault-Detection-in-Power-Microgrid/9c5cc97c3c099701fdb121a2b44ea4fb2acd1367/Project.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Fault-Detection-in-Power-Microgrid 2 | This project presents the concept of fault detection and location in a Power Microgrid making use of the machine learning concepts like Artificial Neural Network. The electronic equipment used in microgrids is in essential need of more secure protection against short circuit faults. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. A fault occurs when two or more conductors come in contact with each other or ground. Ground faults are considered as one of the main problems in power systems and account for more than 80% of all faults. An effective method to detect, isolate, and protect the power microgrid system against the effects of short circuit faults is extremely important. In this project we worked on a highly effective new method to protect the microgrid system using an Artificial Neural Network (ANN) that will detect and find the location of the fault before it affects other parts of the system. It would, therefore, be more dependable for microgrid protection. This protection network is distributed all along the power microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system. This project focuses on detecting faults and identifying the location of the faults on electric power transmission lines in the power microgrid network. 3 | --------------------------------------------------------------------------------