کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7120081 1461458 2018 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Application of extension neural network algorithm and chaos synchronization detection method to partial discharge diagnosis of power capacitor
ترجمه فارسی عنوان
استفاده از الگوریتم شبکه عصبی توسعه و روش تشخیص هرج و مرج به تشخیص جزئی تخلیه خازن قدرت
کلمات کلیدی
خازن برق، تشخیص گسل، شبکه عصبی فرمت روش تشخیص هرج و مرج، تخلیه جزئی، ترانسفورماتور جریان بالا فرکانس، فیلتر عبور بالا تقویت کننده غیرقابل نفوذ، رابط انسان و ماشین،
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
چکیده انگلیسی
The power capacitor is an important equipment of a power system, which must run in a high-temperature and high-voltage environment for a long time, in order to maintain the stability of system. Hence, the failure rate of the power capacitor increases over time. Conventional capacitor testing methods mostly require multiple sensors and signal parameters, which increase system cost and complexity. This study attempted to propose a novel method for capacitor fault recognition. It developed a fault diagnosis system for power capacitor by employing the extension neural network (ENN) algorithm and the chaos synchronization detection method. In terms of signal acquisition, partial discharge was measured by hardware circuits, such as high-frequency current transformer (HFCT), high-pass filter, noninverting amplifier circuit, and high frequency oscillography. The ENN and the chaos method were integrated with hardware circuits to develop a human-machine interface fault diagnosis system designed with LabVIEW. The proposed method was also compared with extension method and artificial neural network algorithm. According to the results, the ENN has the best recognition result, and the huge data could be reduced greatly by the data pre-processing of the chaos synchronization detection method. Any subtle changes in the power capacitor discharge signal could be detected effectively, thus achieving an accurate operating state of the power capacitor.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 129, December 2018, Pages 227-235
نویسندگان
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