Application of artificial neural networks for shunt active power filter control

Mohammed Qasim, Vinod Khadkikar

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


Artificial neural network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN-based control algorithms for shunt APF. A step-by-step procedure to implement these ANN-based techniques in MATLAB/Simulink environment is provided. Furthermore, a detailed analysis on the performance, limitation, and advantages of both methods is presented in the paper.

Original languageBritish English
Article number6812202
Pages (from-to)1765-1774
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number3
StatePublished - Aug 2014


  • Adaptive Linear Neuron (ADALINE)
  • Artificial neural network (ANN)
  • Feed-forward multilayer neural network (MNN)
  • Shunt active power filter (APF)


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