Electrical fault detection and classification based on multiple machine learning algorithms

Electrical faults in the power system refer to a variety of abnormalities in the power equipment, and these faults may lead to equipment damage or even cause dangerous events such as fires and explosions, which pose a serious threat to people's lives and property. Therefore, it is very important to find and deal with these faults in time. In this paper, based on the electrical fault dataset of power system, SVM, decision tree, KNN and random forest model are used to detect electrical faults. After the confusion matrix results are analyzed, most of the predictions of the four models are correct, and only a few instances that should not be predicted as electrical faults are predicted as electrical faults. All the four machine learning models achieved more than 99% accuracy in detecting electrical faults in the power system and were able to detect electrical faults very accurately. Among them, the SVM model has the highest prediction accuracy of 99.6%, followed by the KNN model prediction accuracy of 99.5%. In summary, this paper adopts machine learning method to detect electrical faults in power systems, and achieves better results in the experimental results. This method can help power system supervisors find and deal with faults in time, and improve the safety and stability of the power system. In the future, we can further study how to optimize the model to improve the detection accuracy and apply it in practical production.