Combined Diagnosis of PD Based on the Multidimensional Parameters | Zendy

Mohammad Heidari | Zendy

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Combined Diagnosis of PD Based on the Multidimensional Parameters

Author(s) -

Mohammad Heidari

Publication year - 2016

Publication title -

modelling and simulation in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 20

eISSN - 1687-5591

pISSN - 1687-5605

DOI - 10.1155/2016/5949140

Subject(s) - ultra high frequency , partial discharge , artificial neural network , ultrasonic sensor , entropy (arrow of time) , fault (geology) , ultrasound , computer science , pattern recognition (psychology) , electronic engineering , algorithm , engineering , artificial intelligence , acoustics , physics , electrical engineering , quantum mechanics , voltage , seismology , geology

This paper presents a comprehensive multiparameter diagnosis method based on multiple partial discharge (PD) signals which include high-frequency current (HFC), ultrasound, and ultrahigh frequency (UHF). The HFC, ultrasound, and UHF PD are calculated under different types of faults. Therefor the characteristic values, as nine basic characteristic parameters, eight phase characteristic parameters, and the like are calculated. Diagnose signals are found with the method based on information fusion and semisupervised learning for HFC PD, adaptive mutation parameters of particle entropy for ultrasonic signals, and IIA-ART2A neural network for UHF signals. In addition, integrate the diagnostic results, which are the probability of fault of various defects and matrix, of different PD diagnosis signals, and analysis with Sugeno fuzzy integral to get the final diagnosis

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