Transformer Incipient Hybrid Fault Diagnosis Based on Solar-Powered RFID Sensor and Optimized DBN Approach

Abstract

This paper introduces a novel hybrid fault diagnosis method for power transformer. This method employs solar-powered radio-frequency identification (RFID) sensor for transformer vibration signal acquisition and deep belief network (DBN) for feature extraction. The customized RFID sensor employs solar panel as a power source, and a supercapacitor is adopted to be the stand-by power when the solar panel cannot work. A charging circuit is exploited to guarantee constant DC output voltage. The collected hybrid faults signal is characterized as nonlinear and nonstationary; moreover, it contains abundant noises and harmonic components, which makes it difficult to acquire succinct and robust features from the raw signals. Hence, the DBN is adopted to extract features from the collected vibration signal. In order to obtain optimum feature extraction performance, the quantum particle swarm optimization algorithm (QPSO) is employed to determine the hidden layer structure and learning rate of the DBN model. The experiments indicate that the proposed RFID sensor is able to realize reliable data acquisition and transmission. Besides, the optimized DBN achieves remarkable results in feature extraction for the hybrid fault signal and achieves high diagnosis accuracy.