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Implementation of PSOANN Optimized PI Control Algorithm for Shunt Active Filter

M. Sujith1, *, S. Padma2

1 Department of EEE, IFET College of Engineering, Villupuram, Tamilnadu, 605108, India.
2 Department of EEE, Sona College of Engineering, Salem, 636005, India.

* Corresponding Author: M. Sujith. Email: .

Computer Modeling in Engineering & Sciences 2020, 122(3), 863-888.


This paper proposes the optimum controller for shunt active filter (SAF) to mitigate the harmonics and maintain the power quality in the distribution system. It consists of shunt active filter, Voltage Source Inverter (VSI), series inductor and DC bus and nonlinear load. The proposed hybrid approach is a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) termed as PSOANN. The PI controller gain parameters of kp and ki are optimized with the help of PSOANN. The PSOANN improves the accuracy of tuning the gain parameters under steady and dynamic load conditions; thereby it reduces the values of THD within the prescribed limits of IEEE 519. The PSO optimizes the dataset of terminal voltage and DC voltage present in shunt active filter for different load condition. The optimized dataset acts as the input for the controller to predict the optimal gain with minimal error and to generate the optimized control signal for the SAF. The proposed methodology is modelled and simulated with the help of MATLAB/Simulink platform and illustrated the few test cases considered for exhibiting the performance of proposed hybrid controller. The experimental results are measured with developed laboratory prototype and compared with the simulation results to validate the effectiveness of the proposed control methodology.


Cite This Article

Sujith, M., Padma, S. (2020). Implementation of PSOANN Optimized PI Control Algorithm for Shunt Active Filter. CMES-Computer Modeling in Engineering & Sciences, 122(3), 863–888.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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