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

  • Authors:

  • Jinbang Xu;Jun Yang;Anwen Shen;Junfeng Chen

  • Affiliations:

  • Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ...;Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ...;Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ...;Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ...

  • Venue:
  • International Journal of Wireless and Mobile Computing
  • Year:
  • 2014

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Abstract

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.