Intelligent Diagnosis Method for Dry-type Transformer Status Based on Machine Vision and Auditory Response
A new transformer condition diagnosis method integrating machine vision and auditory response is proposed to enhance the status monitoring and fault diagnosis capability of dry-type transformers.Firstly,potential fault locations of the dry-type transformer are identified and monitored by target detection algorithms to obtain their positional information.Secondly,the acoustic information of the potential fault location is calculated by integrating the delay-and-sum beamforming algorithm,and the acoustic information is used to monitor the occurrence of discharge defects in real time.Finally,for me-chanical faults that cannot be quickly diagnosed,the auditory peripheral model is used to obtain the auditory spectrum.Based on the characteristic frequency bands of the sound signal,multiple feature frequency auditory spectra are obtained,and the transformer fault type is identified by a convolutional neural network.The experimental results show that the pro-posed method has an accuracy rate up to 87.51%for the transformer state recognition under noisy conditions,which is bet-ter than other comparative time-frequency domain feature extraction methods.Therefore,the proposed method can diagnose the state of dry-type transformers more accurately,has better noise resistance feature,and effectively complement the current monitoring methods for transformers.
fault diagnosisdry-type transformerstate detectionmachine visionacoustic imagingauditory periph-ery model
万方数据
维普
