Abstract
In this work a diagnosis system for an electrical network is proposed. The approach carries out the monitoring of an electrical system with dynamical load changes proposed by the IEEE. The framework is composed by two phases. The detection phase which uses a fuzzy system, and the diagnosis phase that computes the Euclidean distances between samples in order to identify a pattern on the system’s elements. The proposal is able to diagnose asymmetrical electrical faults. Promissory results of the proposal are shown.
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Hernández Morales, C.O., Nieto González, J.P. (2013). Fault Detection and Diagnosis of Electrical Networks Using a Fuzzy System and Euclidian Distance. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_19
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DOI: https://doi.org/10.1007/978-3-642-45111-9_19
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