Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations
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‐s...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
|
| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12054 |
| _version_ | 1818513401897287680 |
|---|---|
| author | Tianyan Jiang Xiao Yang Yuan Yang Xi Chen Maoqiang Bi Jianfei Chen |
| author_facet | Tianyan Jiang Xiao Yang Yuan Yang Xi Chen Maoqiang Bi Jianfei Chen |
| author_sort | Tianyan Jiang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T00:00:46Z |
| format | Article |
| id | doaj.art-1c9d0f5f760a4e868c1b692a458e4693 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-12-11T00:00:46Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-1c9d0f5f760a4e868c1b692a458e46932022-12-22T01:28:28ZengWileyCAAI Transactions on Intelligence Technology2468-23222022-06-017229230010.1049/cit2.12054Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substationsTianyan Jiang0Xiao Yang1Yuan Yang2Xi Chen3Maoqiang Bi4Jianfei Chen5School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaSchool of Electrical and Electronic Engineering Chongqing University of Technology Chongqing ChinaDepartment of Electrical and Computer Engineering University of Maryland College Park Maryland USAAbstract 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.https://doi.org/10.1049/cit2.12054gradient methodswavelet transformsant colony optimisationpartial dischargesgenetic algorithmsmean square error methods |
| spellingShingle | Tianyan Jiang Xiao Yang Yuan Yang Xi Chen Maoqiang Bi Jianfei Chen Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations CAAI Transactions on Intelligence Technology gradient methods wavelet transforms ant colony optimisation partial discharges genetic algorithms mean square error methods |
| title | Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations |
| title_full | Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations |
| title_fullStr | Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations |
| title_full_unstemmed | Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations |
| title_short | Wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations |
| title_sort | wavelet method optimised by ant colony algorithm used for extracting stable and unstable signals in intelligent substations |
| topic | gradient methods wavelet transforms ant colony optimisation partial discharges genetic algorithms mean square error methods |
| url | https://doi.org/10.1049/cit2.12054 |
| work_keys_str_mv | AT tianyanjiang waveletmethodoptimisedbyantcolonyalgorithmusedforextractingstableandunstablesignalsinintelligentsubstations AT xiaoyang waveletmethodoptimisedbyantcolonyalgorithmusedforextractingstableandunstablesignalsinintelligentsubstations AT yuanyang waveletmethodoptimisedbyantcolonyalgorithmusedforextractingstableandunstablesignalsinintelligentsubstations AT xichen waveletmethodoptimisedbyantcolonyalgorithmusedforextractingstableandunstablesignalsinintelligentsubstations AT maoqiangbi waveletmethodoptimisedbyantcolonyalgorithmusedforextractingstableandunstablesignalsinintelligentsubstations AT jianfeichen waveletmethodoptimisedbyantcolonyalgorithmusedforextractingstableandunstablesignalsinintelligentsubstations |