Research on Distributed Power Quality Disturbance Detection Based on ILMD
The local mean decomposition method is effective in analyzing non-linear and non-stationary data, and it is suitable for the detection of power quality disturbance signals. The endpoint effect caused by the method is studied, and the original method is improved for the problem that the disturbance s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2019-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/44/e3sconf_icaeer18_02030.pdf |
| _version_ | 1818578657832075264 |
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| author | Liao Xiaohui Xiao Jingbo Wang Zhigang Wang Hao Ning Kang |
| author_facet | Liao Xiaohui Xiao Jingbo Wang Zhigang Wang Hao Ning Kang |
| author_sort | Liao Xiaohui |
| collection | DOAJ |
| description | The local mean decomposition method is effective in analyzing non-linear and non-stationary data, and it is suitable for the detection of power quality disturbance signals. The endpoint effect caused by the method is studied, and the original method is improved for the problem that the disturbance signal cannot be accurately located. An improved Local Mean Decomposition (ILMD) method is proposed. ILMD uses cubic spline interpolation instead of smoothing to obtain local mean function and envelope estimation function. Radial Basis Function (RBF) neural network is used to extend the information at both ends of the data, which improves the endpoint effect. Combined with Hilbert transform, the instantaneous frequency of power quality disturbance signal can be more accurately calculated. The improved method is also applicable to disturbance signals with weak periodic law, and has less requirement for disturbance signal conditions and universal applicability. The effectiveness of ILMD is validated by simulation examples and the measured data of voltage signal at low voltage side of 35kV bus transformer in a wind farm. |
| first_indexed | 2024-12-16T06:49:18Z |
| format | Article |
| id | doaj.art-2885302ac10f49cda15f98c9d91c2d32 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-16T06:49:18Z |
| publishDate | 2019-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-2885302ac10f49cda15f98c9d91c2d322022-12-21T22:40:27ZengEDP SciencesE3S Web of Conferences2267-12422019-01-011180203010.1051/e3sconf/201911802030e3sconf_icaeer18_02030Research on Distributed Power Quality Disturbance Detection Based on ILMDLiao Xiaohui0Xiao Jingbo1Wang Zhigang2Wang Hao3Ning Kang4School of Electrical Engineering, Zhengzhou UniversitySchool of Electrical Engineering, Zhengzhou UniversitySchool of Electrical Engineering, Zhengzhou UniversitySchool of Electrical Engineering, Zhengzhou UniversityZhengzhou Airport Xing gang Investment Group Co., Ltd.The local mean decomposition method is effective in analyzing non-linear and non-stationary data, and it is suitable for the detection of power quality disturbance signals. The endpoint effect caused by the method is studied, and the original method is improved for the problem that the disturbance signal cannot be accurately located. An improved Local Mean Decomposition (ILMD) method is proposed. ILMD uses cubic spline interpolation instead of smoothing to obtain local mean function and envelope estimation function. Radial Basis Function (RBF) neural network is used to extend the information at both ends of the data, which improves the endpoint effect. Combined with Hilbert transform, the instantaneous frequency of power quality disturbance signal can be more accurately calculated. The improved method is also applicable to disturbance signals with weak periodic law, and has less requirement for disturbance signal conditions and universal applicability. The effectiveness of ILMD is validated by simulation examples and the measured data of voltage signal at low voltage side of 35kV bus transformer in a wind farm.https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/44/e3sconf_icaeer18_02030.pdf |
| spellingShingle | Liao Xiaohui Xiao Jingbo Wang Zhigang Wang Hao Ning Kang Research on Distributed Power Quality Disturbance Detection Based on ILMD E3S Web of Conferences |
| title | Research on Distributed Power Quality Disturbance Detection Based on ILMD |
| title_full | Research on Distributed Power Quality Disturbance Detection Based on ILMD |
| title_fullStr | Research on Distributed Power Quality Disturbance Detection Based on ILMD |
| title_full_unstemmed | Research on Distributed Power Quality Disturbance Detection Based on ILMD |
| title_short | Research on Distributed Power Quality Disturbance Detection Based on ILMD |
| title_sort | research on distributed power quality disturbance detection based on ilmd |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/44/e3sconf_icaeer18_02030.pdf |
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