Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations. Issue 2 (16th July 2021)

• Record Type: Journal Article
• Authors: Jiang, Tianyan; Yang, Xiao; Yang, Yuan; Chen, Xi; Bi, Maoqiang; Chen, Jianfei
• Is Part Of: CAAI transactions on intelligence technology. Volume 7:Issue 2(2022)
• Page Start: 292
• Page End: 300
• Publication Date: 2021-07-16
• Subjects: gradient methods -- wavelet transforms -- ant colony optimisation -- partial discharges -- genetic algorithms -- mean square error methods -- signal denoising -- search problems -- substations; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/cit2.12054
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract: Partial discharge (PD) signals are an important index to evaluate the operation state of intelligent substations. The correct distinction of PD pulse and interference pulse has become a challenging task. Because of the noise and the low signal‐to‐noise ratio, the stable signals become non‐stationary. The selection of a wavelet basis, the selection rule of threshold λ and the design of the threshold function are the key factors affecting the final denoising effect. Therefore, an enhanced ant colony optimisition wavelet (ACOW) algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation (ACO) algorithm. At the same time the efficiency of adaptive search calculation, was also significantly improved. The method of the ACOW algorithm was compared with the soft wavelet method, gradient‐based wavelet method and the genetic optimisation wavelet (GOW) method. Using these four methods to denoise four typical signals, different mean square errors (MSE), magnitude errors (ME) and time costs were obtained. Interestingly, the results show that the ACOW method can achieve the minimum MSE and has less time cost. It generates significantly smaller waveform distortion than the other three threshold estimation methods. In addition, the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.