Quantitative analysis and evaluation of the optimization method of transformer partial discharge ultrasonic sensor monitoring

Abstract

Abstract Accurate partial discharge (PD) measurement is critical to ensure the stable operation of transformers. The ultrasonic method is a low-cost, safe, and reliable technology that is widely available and provides real-time monitoring capability. The PD ultrasonic signals propagation is complex and severely attenuated in the transformer, which greatly affects the measurement accuracy of the sensor. In order to improve the accurate monitoring of PD in complicated transformer environments, an optimization monitoring method based on sub-scene detection and quantitative analysis and evaluation is proposed in this paper. Firstly, to address this concern, a sub-scene monitoring method is designed and explores the optimal monitoring points separately. In addition, establish the partition model of an oil-immersed power transformer, and compare the ultrasonic wave propagation characteristics and sound pressure attenuation characteristics of different monitoring points. Then, analyzed by wavelet transform algorithm and Pearson correlation coefficient to determine the best monitoring point location for each scene. Finally, we further tested the proposed method through extensive experiments based on simulations, testbed, and trial deployment. The experimental results have demonstrated the feasibility and accuracy of the proposed method in transformer PD monitoring under complicated environments.