Application of the Variational Mode Decomposition for Power Quality Analysis

Abstract

Harmonics and interharmonics in power systems distort the grid voltage, deteriorate the quality and stability of the power grid. Therefore, rapid and accurate harmonic separation from the grid voltage is crucial to power system. In this article, a variational mode decomposition-based method is proposed to separate harmonics and interharmonics in the grid voltage. The method decomposes the voltage signal into fundamental, harmonic, interharmonic components through the frequency spectrum. An empirical mode decomposition (EMD) and an ensemble empirical mode decomposition (EEMD) can be combined with the independent component analysis (ICA) to analyze the harmonics and intherharmonics. By comparing EMD-ICA, EEMD-ICA methods, the proposed method has several advantages: (1) a higher correlation coefficient of all the components is found; (2) it requires much less time to accomplish signal separation; (3) amplitude, frequency, and phase angle are all retained by this method. The results obtained from both synthetic and real-life signals demonstrate the good performance of the proposed method.

Keywords:

• EEMD-ICA
• EMD-ICA
• harmonics
• interharmonics
• power system
• variational mode decomposition (VMD)

Additional information

Funding

The authors gratefully acknowledge the support of the Foundation of Liaoning Province Education Administration (grant number L201609), the Doctoral Start-up Foundation of Liaoning Province (grant number 20170520191) and the Royal Society U.K.

Notes on contributors

Kewei Cai

Kewei Cai received the B.S., and Ph.D. degrees from the School of Electrical Engineering, Dalian University of Technology (DLUT), Dalian, China, in 2007, and 2014, respectively. He is a visiting scholar in Aston University, UK, from Sept. 2018 to Aug. 2019. Presently, he is a lecturer at Dalian Ocean University (DLOU). His fields of interest are artificial intelligence technic in power system, power electronics, Wind power generation and Electrified vehicles.

Wenping Cao

Wenping Cao was a Ph.D. Student with University of Nottingham, UK in 2003. He became a Research Fellow in 2006, a Senior Lecturer in 2010, and the Chair Professor of Electrical Power Engineering, School of Engineering and Applied Science, Aston University in 2015. Professor Cao is a Senior Member of the Institute of Electrical and Electronics Engineers. His current research includes electric machines and drives, power electronics, Wind power generation, Electrified vehicles, Measurement techniques and Power system analysis.

Zheng Liu

Zheng Liu received the B.S. degree from the Department of Electrical Engineering, Huazhong University of Science & Technology (HUST), Wuhan, China, in 2009. He received the M.S., Ph.D. degrees from The University of Sheffield, in 2011, and Newcastle University in 2016, respectively. He was a research staff in Aston University, UK, from Mar. 2016 to Feb. 2018. Presently, he is a post-doctor at Dalian University of Technology (DLUT). His fields of interest are electric machines and drives, power electronics and Wind power generation.

Wei Wang

Wei Wang received the B.S., and Ph.D. degrees from Shenyang University of Technology, Shenyang, China, in 2004, and Northeastern University, Shenyang, China, in 2011, respectively. Presently, she is an associate professor at Dalian Ocean University (DLOU). Her fields of interest are artificial intelligence soft sensing technic.

Guofeng Li

Guofeng Li was a Ph.D. Student with Dalian University of Technology (DLUT), Dalian, China, in 2000. Presently, he is a professor at DLUT. Professor Li is the dean of the School of Electrical Engineering, Dalian University of Technology (DLUT). His current research includes electric machines and drives, power electronics, Wind power generation, Electrified vehicles, Measurement techniques and Power system analysis.