摘要
Abstract
To solve the de-noise problem of metal oxide arrester (MOA) leakage current, a PSO (particle swarm optimization)-based wavelet de-noising algorithm is proposed. Firstly, the db5 wavelet is used to decompose the leakage current. Secondly, the threshold value is set and the processed wavelet coefficients are reconstructed. Finally, de-noising is achieved through PSO threshold value and the results are verified through MOA current modelling. Studies show that by decomposing the leakage current with db5 and optimizing the threshold value with PSO, the values ofc5,c4,c3,c2 andc1 are found to be 0.32, 0.20, 0.13, 0.02 and 0.01, respectively. The SNR (signal to noise ratio) after de-noise is raised by 7dB compared with the result when just using the stationary wavelet. The results indicate that the de-noising effect of PSO-based wavelet de-noising algorithm is better than that of wavelet de-noising algorithm.关键词
诊断/老化/粒子群算法/小波算法/消噪/漏电流/金属氧化物避雷器Key words
diagnose/aging/particle swarm/wavelet/de-noise/leakage current/MOA分类
信息技术与安全科学
