Deep SqueezeNet Based Technique for Detection of High Impedance Arcing Faults in Electric Power Distribution Networks

Abstract

High Impedance Arcing Faults (HIAF) have always been considered an influential factor in the protection of electric power distribution networks (EPDNs). Characteristics such as low current levels in these faults causes the malfunction of conventional protection devices because of incorrect detection. Therefore, new methods should be provided that are able to detect the HIAF from other events in the EPDN based on these characteristics. Most of the previous fault detection techniques are dependent on a massive volume of training data to detect and classify the faults and other events, requiring a lot of time for data extraction. Furthermore, in some cases accessibility to these data is too difficult and sometimes impossible. Therefore, this paper proposes a novel protection technique based on a deep-learning algorithm to detect and classify the HIAF from other events, and also to significantly reduce the dependence on a large amount of training data. The proposed technique uses a small amount of data to extend the knowledge of pretrained SqueezeNet architecture to HIAF detection and classification problems, thereby reducing the dependence of the method on a large amount of training data. The simulation results in the presence of renewable energy sources on the modified IEEE 13-bus and 34-bus EPDNs indicate the high accuracy of the proposed technique in categorizing different network events. © 2024 IEEE.