Artificial Neural Networks-Based Fault Localization in Distributed Generation Integrated Networks Considering Fault Impedance

Abstract

This paper proposes a fault location and protection method for power distribution system with Distributed generation (DG) resources, considering fault impedance. A multi-layer perceptron (MLP) Artificial Neural Network (ANN)-based approach using the Levenberg-Marquardt algorithm to train the neural network for fault location and identification within distribution networks with DGs is developed. The proposed method emphasizes the incorporation of fault impedance in the analytic process. It also leverages the robust computational capabilities and inherent simplicity of ANNs to accurately detect the fault type and subsequently identify the fault location. Furthermore, it implements a zoning strategy, which partitions the distribution system into multiple independent sections to facilitate a more organized and effective fault management process. Simulation results and conducted analyses confirmed the effectiveness of the proposed approach in localizing faults within the network thereby minimizing downtime during faulty conditions and improving power system's capabilities and reliability. Among the key advantages of the proposed fault detection approach are its ability to adeptly handle the complexities introduced by the wide integration of DGs in distribution networks.