粒子群优化的小波算法在避雷器老化诊断中的应用

为研究金属氧化物避雷器漏电流“消噪”问题，提出一种粒子群优化的小波消噪方法。首先利用db₅小波对漏电流信号进行5层分解，其次设定待求解阈值，对信号进行重构，最后通过粒子群优化求解阈值实现消噪，并通过模拟MOA小电流区模型进行仿真验证。研究表明：使用db₅小波对信号进行分解，并利用PSO对阈值进行优化求解，最终阈值c₅，c₄，c₃，c₂，c₁分别为0.32，0.20，0.13，0.02和0.01。消噪后信噪比相对于单独使用平稳小波提升了7dB，说明利用结合粒子群优化的小波消噪算法进行消噪，消噪效果明显优于单独使用小波消噪算法。

Application of PSO-based Wavelet Algorithm in MOA Aging Diagnosis

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 of c₅, c₄, c₃, c₂ and c₁ 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.