基于主成分分析法的变压器直流偏磁声学异常检测

Detection of DC Bias Magnetic Acoustic Abnormality of Transformer Based on Principal Component Analysis

针对变压器故障在线检测问题,利用声学无入侵方式对变压器进行检测。变压器种类繁多、检测结果复杂,单凭听觉无法有效对变压器进行异常检测,针对变压器声学信号数据规模大、维度高、实时性要求高的问题提出解决方案。对采集到的北京市某变电站变压器正常运行和异常时的声学信号进行去噪处理。利用梅尔频率倒谱系数方法提取信号MFCC特征,分别在无监督和半监督学习模式下通过主成分分析法对变压器声学信号进行故障检测,并比较两种模式下主成分分析法对变压器故障监测的效果。实验结果表明,基于主成分分析法的变压器声学异常检测具有良好的检测能力,在半监督学习模式下,主成分分析法具有更好的检测效果。

Aiming at the problem of on-line detection of transformer faults, a non-invasive acoustic method is proposed to detect transformers. There are many types of transformers, and the test results are complex. Auditory alone cannot effectively detect abnormalities in transformers. A solution is proposed to solve the problems of large-scale, high dimension, and high-real-time requirements of transformer acoustic signal data. Using the collected acoustic signals of a transformer in a substation in Beijing during normal operation and abnormal operation is denoised. Using the Mel frequency cepstrum coefficient method,in the unsupervised and semi-supervised learning mode, the principal component analysis(PCA) method is used to detect the fault of the transformer acoustic signal and the effect of the PCA method on the transformer fault monitoring in the two modes is compared. The experimental results show that the transformer acoustic anomaly detection based on the principal component analysis method has a good detection ability, and in the semi-supervised learning mode, the principal component analysis method has a better detection effect.