Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter

Cheng I. Chen, Yeong Chin Chen, Chung Hsien Chen

研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(Scopus)

摘要

The usage of a shunt active power filter (SAPF) is one of the helpful means to mitigate the reactive power and harmonic current of a power grid. The compensation performance of the SAPF is related to the accuracy of the reference voltage extraction from the utility grid, the control stability of the DC-link voltage regulation, and the synchronization between the source voltage and the reference compensation current. To modify the performance of the SAPF for the harmonic compensation, the control strategy of the SAPF reference compensation current based on the recurrent wavelet fuzzy neural network (RWFNN) is proposed in this paper. There are three sections in the proposed control strategy, including the regulated fundamental positive-sequence extraction (section A), DC-link voltage regulation (section B), and calculation of reference compensation current (section C). By regulating the analysis mechanism with the variation of fundamental frequency in the section A, the accurate reference voltage would be obtained. The control stability for the regulation of the DC-link voltage can be accomplished by applying the RWFNN-based controller in the section B. With the synchronized reference voltage in the section A and the estimated control current in the section B, the reference compensation current can be correctly obtained in the section C. From the case studies with the real-time simulator produced by OPAL-RT Technologies Inc., the effectiveness of proposed control strategy for the SAPF reference compensation current can be verified.

原文???core.languages.en_GB???
文章編號8687
期刊Energies
15
發行號22
DOIs
出版狀態已出版 - 11月 2022

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