OLTC Fault detection Based on Acoustic Emission and Supported by Machine Learning

被引:12
作者
Cichon, Andrzej [1 ]
Wlodarz, Michal [1 ]
机构
[1] Opole Univ Technol, Fac Elect Engn Automat Control & Informat, Dept Elect Power & Renewable Energy, PL-45758 Opole, Poland
关键词
on-load tap changer; acoustic emission; wavelet decomposition; diagnostic method; VIBRATION SIGNAL;
D O I
10.3390/en17010220
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Power transformers are an essential part of the power grid. They have a relatively low rate of failure, but removing the consequences is costly when it occurs. One of the elements of power transformers that are often the reason for shutting down the unit is the on-load tap changer (OLTC). Many methods have been developed to assess the technical condition of OLTCs. However, they require the transformer to be taken out of service for the duration of the diagnostics, or they do not enable precise diagnostics. Acoustic emission (AE) signals are widely used in industrial diagnostics. The generated signals are difficult to interpret for complex systems, so artificial intelligence tools are becoming more widely used to simplify the diagnostic process. This article presents the results of research on the possibility of creating an online OLTC diagnostics method based on AE signals. An extensive measurement database containing many frequently occurring OLTC defects was created for this research. A method of feature extraction from AE signals based on wavelet decomposition was developed. Several machine learning models were created to select the most effective one for classifying OLTC defects. The presented method achieved 96% efficiency in OLTC defect classification.
引用
收藏
页数:14
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