Pattern Recognition of Growth Characteristics Based on UHF PD Signals of Electrical Tree
Lili Li, Yulong Wang, Junguo Gao, Ning Guo, Gang Han
Abstract
In insulation system of power equipment, electrical tree grown under the effect of partial discharges (PD) is one of the main degradation processes leading to failure of high voltage polymeric insulation. PD signals are measured and analyzed for condition monitoring of electrical insulation. In this paper, the combine detection system of Ultra-High Frequency (UHF) PD and electrical tree was used to test PE samples. Based on the energy amplitude variation, the average energy and the average energy distribution of PD signals to identify the growth process of electrical tree, twenty-eight PD characteristic parameters were used as input parameters, such as the energy percentage of 16-layer wavelet decomposition, the wavelet coefficient parameter and the time-frequency domain parameter, which entered the three-layer Back Propagation Neural Network (BPNN) of forty-five hidden neurons, and then the accuracy of pattern recognition during the growth process of electrical tree can reach 99.93% after 5000 steps of training cycle. The consistency of calculation results and experimental results presents that the calculation method can reveal the relationship between PD signals and the growth of electrical tree, which lays the foundation for the process analysis of electrical tree and insulation state monitoring.