A novel ANN-based harmonic extraction method tested with ESN, RNN and MLP in shunt active power filters

被引：1

作者：

Xu, Jinbang ^{[1
]}

Yang, Jun ^{[1
]}

Shen, Anwen ^{[1
]}

Chen, Junfeng ^{[1
]}

机构：

[1] Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology

来源：

International Journal of Wireless and Mobile Computing | 2014年 / 7卷 / 02期

关键词：

ANN; Artificial neural networks; Echo state networks; ESN; Harmonic extraction; MLP; Multilayer perceptron networks; Recurrent neural networks; RNN; SAPF; Shunt active power filters;

D O I：

10.1504/IJWMC.2014.059708

中图分类号：

学科分类号：

摘要：

With the wide use of power conversion devices - 'nonlinear loads' - many harmonic currents are being injected into the power grid. Shunt Active Power Filters (SAPF) are the power electronic equipment to compensate the harmonic currents caused by nonlinear loads. As the foundation of the harmonics recognition and compensation, harmonic extraction is the key technology in SAPF. Artificial Neural Networks (ANN) method has the features of parallel computation and satisfactory results for distorted source voltages over traditional extraction methods. This paper proposes a new harmonic extraction method based on ANN. To test the feasibility of different types of neural networks in this application, this paper compares the performances of three types of ANN: Echo State Networks (ESN), Recurrent Neural Networks (RNN) and Multilayer Perceptron Networks (MLP). © 2014 Inderscience Enterprises Ltd.

引用

页码：123 / 131

页数：8