Detection of faults in electrical power grids using an enhanced anomaly-based method
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The increasing demand on electrical power consumption all over the world makes the need of stable and reliable electrical power grids is indispensable. However, one of hostile obstacles which delays reaching out to that desired goal is occurrence of faults. Despite to fact that dozens of studies have been put forward to detect electrical faults, these studies still suffer from several downsides such as validation and automation. In this paper, an electrical fault detection system based on the concept of anomaly detection is presented. The main salient advantages of the proposed system are overcoming the limitations of existed counterpart systems and its compatibility with real-world power grids. To enhance the performance of the proposed system, two vital stages are involved in its design prior to training, namely, data preprocessing and pre-training. Whereas the former is to prepare raw signals to be modeled, the latter is dedicated for model’s hyperparameter selection using the particle swarm optimization metaheuristic. Moreover, two well-known anomaly detection models, namely, One-Class Support Vector Machines and principal component analysis are utilized to validate the proposed system as well as real-time data (VSB dataset) are used to train and test models. Finally, the experimental results and discussion emphasize that there is a performance improvement in detecting of electrical faults when using the proposed system.