Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

Abstract

The use of sensitive electrical gadgets in industries, buildings, smart cities, and homes has increased drastically in recent years. PQ events such as interruptions, surges, and sags have a high impact on these sensitive devices. The failure of these delicate devices in real-time applications, particularly smart applications, may result in significant damage. The supply quality decreases because of the failure of internal transmission system elements, unbalanced loads, and other outdoor issues such as like weather. Several academics have proposed techniques to analyze these PQ disturbances, including wavelet packets, S-transform, rough sets and neural networks. In all the available algorithms, the classification procedure involves the extraction of a large set of features from the transformed outputs, training the classifier, and finally making a conclusion with the classifier. Because of the involvement of a large number of features, the computational cost of all these methods increases. To reduce complexity and enhance classification efficiency, the proposed method focuses on extracting fewer low-complexity wavelet features from signals. Pattern recognition (PR) methods, such as the wide variety of K-nearest neighbors (KNN) and ensemble classifiers, are used to classify PQ events in this study. The performance of the proposed ML approaches' performance is evaluated at various training and testing rates. Subsequently, the performance of the proposed strategies was compared to that of the current methods to determine the dominance of the proposed approaches.