Enhanced anomaly-based fault detection system in electrical power grids

Abstract

Early and accurate fault detection in electrical power grids is a very essential research area because of its positive influence on network stability and customer satisfaction. Although many electrical fault detection techniques have been introduced during the past decade, the existence of an effective and robust fault detection system is still rare in real-world applications. Moreover, one of the main challenges that delays the progress in this direction is the severe lack of reliable data for system validation. Therefore, this paper proposes a novel anomaly-based electrical fault detection system which is consistent with the concept of faults in the electrical power grids. It benefits from two phases prior to training phase, namely, data preprocessing and pretraining. While the data preprocessing phase executes all elementary operations on the raw data, the pretraining phase selects the optimal hyperparameters of the model using a particle swarm optimization (PSO)-based algorithm. Furthermore, the one-class support vector machines (OC-SVMs) and the principal component analysis (PCA) anomaly-based detection models are exploited to validate the proposed system on the VSB dataset which is a modern and realistic electrical fault detection dataset. Finally, the results are thoroughly discussed using several quantitative and statistical analyses. The experimental results confirm the effectiveness of the proposed system in improving the detection of electrical faults.