Multiple fault diagnosis in electrical power systems with dynamic load changes using soft computing

Abstract

Power systems monitoring is particularly challenging due to the presence of dynamic load changes in normal operation mode of network nodes, as well as the presence of both continuous and discrete variables, noisy information and lack or excess of data. In this domain, the need to develop more powerful approaches has been recognized, and hybrid techniques that combine several reasoning methods start to be used. The present work is a variant of a proposal made by the author. This paper proposes a fault diagnosis framework that is able to locate the set of nodes involved in multiple fault events. The proposal is a methodology based on the system history data. It detects the faulty nodes, the type of fault in those nodes and the time when it is present. The framework is composed of two phases: In the first phase a probabilistic neural network is trained with the eigenvalues of voltage data collected during normal operation, symmetrical and asymmetrical fault disturbances. The second phase uses an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to give the final diagnosis. A set of simulations are carried out over an electrical power system proposed by the IEEE. To show the performance of the approach, a comparison is made against two different diagnostic systems. © 2013 Springer-Verlag.