کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7123355 | 1461497 | 2016 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis
ترجمه فارسی عنوان
تجمع هیدروژن تغییر یافته ذرات تکاملی زمان-تغییر ضریب شتاب دهی-شبکه عصبی مصنوعی برای تشخیص گسل قدرت ترانسفورماتور
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کلمات کلیدی
بهینه سازی ذرات اصلاح شده، شبکه های عصبی مصنوعی، ترانس برق، هوش مصنوعی،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
In power transformer fault diagnosis, dissolved gas analysis (DGA) has been widely used to identify the type of the fault. The common methods of DGA are IEC 60599 method, Doenenberg's ratio method and Roger's ratio method. The accuracy of the DGA diagnosis will determine the cost, duration and workload of the maintenance since it can influence the error in the maintenance. Although DGA methods have been used widely, sometimes they still yield incorrect diagnosis results. Thus, many works on transformer fault diagnosis have been proposed previously, which include artificial intelligence methods, to improve the accuracy of transformer fault diagnosis. However, the accuracy of the previously reported works is believed to have rooms for improvement. Therefore, in this work, hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient (MEPSO-TVAC)-artificial neural network (ANN) was proposed for transformer fault diagnosis based on dissolved gas data. This is due to these two methods have never been proposed for transformer fault diagnosis in the past. The performance of the ANN was optimised through the proposed MEPSO-TVAC. The superiority of the proposed method was demonstrated through comparison with the existing DGA methods, unoptimised ANN and previously reported methods in literatures. The comparison shows that the proposed hybrid MEPSO-TVAC-ANN obtained the highest accuracy among all methods, which can then be used for power transformer fault diagnosis.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 90, August 2016, Pages 94-102
Journal: Measurement - Volume 90, August 2016, Pages 94-102
نویسندگان
Hazlee Azil Illias, Xin Rui Chai, Ab Halim Abu Bakar,