An approach for localizing abnormal sound sources in converter stations using mobile microphone arrays

Abstract

The acoustic signals generated by power equipment such as converter transformers and AC filters tend to contain crucial status information. Traditional methods relying on manual auscultation to locate equipment faults suffer from subjectivity， low efficiency， and limited reliability. Therefore， a novel approach is proposed， utilizing mobile microphone arrays for abnormal sound source localization. The derivation of the error formula for the translated short-time Fourier transform （STFT） quantitatively assesses the reasonable range of array movement speed and sampling time. In the frequency domain， a positional function relationship between the mobile microphone array and the sound source is established. Building upon this new function relationship， the traditional beamforming algorithm is optimized. A triangulation method is introduced to address the limitation of the beamforming algorithm， which can only determine direction， enabling precise localization of abnormal sound sources in converter stations. Based on on-site test data， a simulation model is constructed to validate the accuracy of the approach for localizing abnormal sound sources in converter stations using mobile microphone arrays.

Keywords

• mobile microphone arrays
• sound source localization
• converter station
• intelligent inspection
• beamforming