Hybrid classifier for fault location in active distribution networks

• Main Authors: Sadegh Jamali, Alireza Bahmanyar, Siavash Ranjbar
• Format: Article
• Language: English
• Published: SpringerOpen 2020-08-01
• Series: Protection and Control of Modern Power Systems
• Subjects: Artificial neural networks; Distributed generation; Distribution networks; Fault location; K-nearest neighbors
• Online Access: http://link.springer.com/article/10.1186/s41601-020-00162-y

Abstract This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.