Title | Research on a General Fast Analysis Algorithm Model for Pd Acoustic Detection System: The Algorithm Model Design and Its Application |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Si, Wen-Rong, Fu, Chen-Zhao, Gao, Kai, Zhang, Jia-Min, He, Lin, Bao, Hai-Long, Wu, Xin-Ye |
Conference Name | 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA) |
Keywords | 3G mobile communication, acoustic detection, acoustic emission, Acoustic Fingerprints, acoustical emission signals, algorithm model design, Automation, Backpropagation, BP artificial neural network, composability, fault diagnosis, gas insulated substations, genetic algorithm model design, genetic algorithms, GIS, Human Behavior, identification with phase compensation, IEC standards, intelligent analysis, IPC, maintenance engineering, maintenance personnel, neural nets, pattern identification, PD acoustic detection system, power engineering computing, PRPD 2D histogram, pubcrawl, Resiliency, Smart grids, standard GIGRE D1.33 444, standard IEC TS 62478-2016, statistic operators |
Abstract | Nowadays, the detection of acoustical emission is widely used for fault diagnosis of gas insulated substations (GIS) in normal operation and factory tests, which is called 'non-conventional' method recommended in the standard IEC TS 62478-2016 and GIGRE D1.33 444. In this paper, to develop a data analyzer for acoustic detection (AD) system to make an assistant diagnosis for technical personnel or equipment operation and maintenance personnel, based on the previous research on the experimental research, pattern identification with phase compensation and the software development, the algorithm model design and its application is given in detail. For the acoustical emission signals (n, ti, qi), the BP artificial neural network optimized by genetic algorithm (GA-BP) is used as a classifier based on the fingerprint consisting of several statistic operators, which are derivate form typical 2D histograms of PRPD with identification with phase compensation (IPC). Experimental results show that the comprehensive algorithm model designed for identification is practical and effective. |
DOI | 10.1109/ICSGEA.2019.00014 |
Citation Key | si_research_2019-1 |