Classification of Electrical Disturbances in Real Time Using Neural Networks

Abstract

Power-quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. This paper presents a novel system based on neural networks for the classification of electrical disturbances in real time. In addition, an electrical pattern generator has been developed in order to generate common disturbances which can be found in the electrical grid. The classifier obtained excellent results (for both test patterns and field tests) thanks in part to the use of this generator as a training tool for the neural networks. The neural system is integrated on a software tool for a PC with hardware connected for signal acquisition. The tool makes it possible to monitor the acquired signal and the disturbances detected by the system.