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Use of Artificial Neural Networks for Location of Defective Insulators in Power Lines
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作者 Renato Massoni Capelini jose feliciano adami +2 位作者 Manuel Luis Barreira Martinez Marcel Femando da Costa Parentoni Ithamar Sene 《Journal of Energy and Power Engineering》 2012年第8期1308-1314,共7页
This is an extended version of the same titled paper presented at the 21st CIRED. It discusses a new technique for identification and location of defective insulator strings in power lines based on the analysis of hig... This is an extended version of the same titled paper presented at the 21st CIRED. It discusses a new technique for identification and location of defective insulator strings in power lines based on the analysis of high frequency signals generated by corona effect. Damaged insulator strings may lead to loss of insulation and hence to the corona effect, in other words, to partial discharges. These partial discharges can be detected by a system composed of a capacitive coupling device (region between the phase and the metal body of a current transformer), a data acquisition board and a computer. Analyzing the waveform of these partial discharges through a neural network based software, it is possible to identify and locate the defective insulator string. This paper discusses how this software analysis works and why its technique is suitable for this application. Hence the results of key tests performed along the development are discussed, pointing out the main factors that affect their performance. 展开更多
关键词 Corona effect current transformer insulator string neural network power line.
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