Harmonic Amplification Damping Using a DSTATCOM-based Artificial Intelligence Controller

Author:

Mejeed Raghad Ali1,Jameil Ahmed K.2,Hussein Husham Idan1

Affiliation:

1. Department of Electrical Power and Machines Engineering, College of Engineering, University of Diyala, Diyala, Iraq

2. Department of Computer Engineering, College of Engineering, University of Diyala, Diyala, Iraq

Abstract

Background & Objective: Harmonic amplification is one of the primary issues in power system networks. The objective of this study is to manage the harmonic event and its significant effects on power quality. A new control approach that uses Artificial Intelligence (AI) is proposed and applied to a Distribution Static Synchronous Compensator (DSTATCOM). DSTATCOM is a FACTS device that can achieve highly effective reactive power compensation to reduce and/or damp the harmonic amplification in power system networks. Results & Conclusion: Simulation results are obtained using the MATLAB/Simulink package. The validity and effectiveness of using the AI approach are proven based on the DSTATCOM FACTs device with linear and nonlinear loads. Analysis results are discussed.

Publisher

Bentham Science Publishers Ltd.

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Computer Science Applications

Reference20 articles.

1. Akagi H.; Control strategy and site selection of a shunt active filter for damping of harmonic propagation in power distribution systems. IEEE Trans Power Deliv 1997,12(1),354-363

2. Singh R.S.; Singh D.K.; Simulation of D-STATCOM for voltage fluctuation. 2 Int Conf Adv Comput Commun Tech 2012; pp. 226-31.

3. Vazquez P.S.; Active power filter control using neural network technologies IEE Proc-Electric Power Appl 54(1): 61-76.

4. Abdeslam D.O.; Wira P.; Flieller D.; A unified artificial neural network architecture for active power filters. IEEE Trans Ind Electron 2012,54(1),61-76

5. Lai L.L.; Intelligent system applications in power engineering: evolutionary programming and neural networks John Wiley & Sons, Inc. New York, NY, USA, 1998; pp. 264.

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