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Localization of mechanical and electrical defects in dry-type transformers using an optimized acoustic imaging
Zhanxi Zhang1, Youyuan Wang1, Zhihe Li1,2
1State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing, People's Republic of China.
None:
This paper presents an acoustic imaging localization system designed to pinpoint common defects in dry-type transformers by analyzing the unique sounds they produce during operation. The system includes an optimized microphone array and an improved multiple signal classification algorithm. Sound signal characteristics of typical defects, such as foreign object intrusion, screw loosening, and partial discharge, are investigated. A 64-element, 8-arm spiral microphone array is designed using a particle swarm optimization algorithm. The multiple signal classification algorithm enhances acoustic imaging quality in field environments by transforming the input from time-domain to preprocessed frequency-domain signals. The power spectra of subarray and main array are combined, forming the optimization algorithm's output. Experimental results demonstrate the system's effectiveness and accuracy.
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