Acoustic array sensor device for partial discharge detection is widely used in power equipment inspection with the advantages of non-contact and precise positioning compared with partial discharge detection methods such as ultrasonic method and pulse current method. However, due to the sensitivity of the acoustic array sensor and the influence of the equipment operation site interference, the acoustic array sensor device for partial discharge type diagnosis by phase resolved partial discharge (PRPD) map might occasionally presents incorrect results, thus affecting the power equipment operation and maintenance strategy. The acoustic array sensor detection device for power equipment developed in this paper applies the array design model of equal-area multi-arm spiral with machine learning fast fourier transform clean (FFT-CLEAN) sound source localization identification algorithm to avoid the interference factors in the noise acquisition system using a single microphone and conventional beam forming algorithm, improves the spatial resolution of the acoustic array sensor device, and proposes an acoustic array sensor device based on the acoustic spectrogram. The analysis and diagnosis method of discharge type of acoustic array sensor device can effectively reduce the system misjudgment caused by factors such as the resolution of the acoustic imaging device and the time domain pulse of the digital signal, and reduce the false alarm rate of the acoustic array sensor device. The proposed method is tested by selecting power cables as the object, and its effectiveness is proved by laboratory verification and field verification.
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