Modeling of faults in the CEB electrical transmission network by approaches: KNN, Random Forests, logistic regression, SVM, ANN and gradient boosting of supervised learning

The work accumulated in this article presents the results of learning the faults that affect the CEB network. The objective is to predict failures in order to prevent these faults from creating interruptions. The network operating data from 2008 to 2015 are used as materials. The algorithms: SVM, KNN, Random Forest, Gradient Boosting, ANN and Logistic Regression were used as methods to create the models. The results are subjected to evaluation criteria namely: the confusion matrix, the area under the ROC curve and the scores (Accuracy, F1 Score, Precision and recall). A characterization of the faults is carried out. The results of the characterization reveal that there are 19 faults and the most recurrent is the short circuit, which appeared 947 times out of 2427 during the study period. The modeling results are perfect. The True Positives of the confusion matrices are greater than 450 out of 497, for the classes. Some are better than others. The unfavorable is obtained through the KNN with AUC=0.761. Its score, (Accuracy=0.955; F1 Score = 0.957; Precision=0.958; Recall=0.955), confirms this observation. The AUC=0.664, remains even more unfavorable with the SVM modeling but its score, (Accuracy=0.989; F1 Score = 0.988; Precision=0.988; Recall=0.989), exceeds that of KNN. Moreover, for the other models, their AUC exceeds 80% with the more perfect logistic regression giving: AUC=0.991; Accuracy=0.991; F1 Score = 0.991; Precision=0.991; Recall=0.991. These results confirm that, even very random and of various causes, we can predict the defects in the CEB network. However, it is necessary to use more recent data in order to apply these results in future operations.