New ANN-Based Algorithms for Detecting HIFs in Multigrounded MV Networks

Abstract

Application of two new ANN-based algorithms for arcing high impedance fault (HIF) detection in multigrounded medium-voltage (MV) distribution networks is presented in this paper. The paper provides an evaluation of two new structures of artificial neural networks (ANNs) that may be used for reliable HIF detection in multigrounded as well as isolated, compensated, and grounded via small resistance distribution grids. The results obtained by use of both neural nets are presented. The performance was tested using data obtained from staged HIFs in real MV network as well as from electromagnetic transients program-alternative transients program simulations. A small number of necessary neurons in developed ANNs, short measuring sliding data window, and easy interpretation of obtained output signals are the main advantages of the proposed approach. Satisfactory results of ANN performance were observed for all examined HIF cases in which the ground fault current was greater than 16 A. The selected ANNs of best performance show high reliability and immunity to transients resulting from switching operations in protected feeders and from capacitor bank switching.