Modified MUSIC algorithm.

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.

本文提出一款声学成像定位系统，旨在通过分析干式变压器（dry-type transformers）运行时产生的特有声响，精准定位其常见故障。该系统搭载优化设计的麦克风阵列与改进型多重信号分类（multiple signal classification）算法。研究团队针对异物侵入、螺丝松动及局部放电等典型故障的声信号特征展开了研究。本研究采用粒子群优化（particle swarm optimization）算法，设计了一款64阵元、8臂螺旋式麦克风阵列。该多重信号分类算法将输入信号从时域转换至预处理后的频域信号，以此提升野外环境下的声学成像质量。研究中对子阵列与主阵列的功率谱进行融合，以此构成该算法的输出结果。实验结果验证了该系统的有效性与定位精度。