Integrated Fault Location and Power-Quality Analysis in Electric Power Distribution Systems

• Autor(a) principal: Bíscaro, A. A.P.
• Data de Publicação: 2016
• Outros Autores: Pereira, R. A.F. [UNESP], Kezunovic, M., Mantovani, J. R.S. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/TPWRD.2015.2464098; http://hdl.handle.net/11449/172837
• topic: Fault location; neural networks; pattern classification; power distribution; power quality (PQ); wavelet transforms

This paper presents a methodology for automated disturbance analysis and fault location on electric power distribution systems using a combination of modern techniques for network analysis, signal processing, and intelligent systems. New algorithms to detect, classify, and locate power-quality disturbances are developed. The continuous process of detecting these disturbances is accomplished through statistical analysis and multilevel signal analysis in the wavelet domain. The behavioral indices of the current and voltage signals are extracted by employing the discrete wavelet transform, multiresolution analysis, and the concept of signal energy. These indices are used by a number of independent Fuzzy-ARTMAP neural networks, which aim to classify the fault type and the power-quality events. The fault location is performed after the classification process. A real life three-phase distribution system with 134 nodes - 13.8 kV and 7.065 MVA - was used to test the proposed algorithms, providing satisfactory results, attesting that the proposed algorithms are efficient, fast, and, above all, intelligent.