| Issue |
E3S Web Conf.
Volume 118, 2019
2019 4th International Conference on Advances in Energy and Environment Research (ICAEER 2019)
|
|
|---|---|---|
| Article Number | 02030 | |
| Number of page(s) | 6 | |
| Section | Energy Equipment and Application | |
| DOI | https://doi.org/10.1051/e3sconf/201911802030 | |
| Published online | 04 October 2019 | |
Research on Distributed Power Quality Disturbance Detection Based on ILMD
1
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
2
Zhengzhou Airport Xing gang Investment Group Co., Ltd., Zhengzhou 451161, China
* Corresponding author:1431203619@qq.com
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.
© The Authors, published by EDP Sciences, 2019
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