Abstract—Power distribution infrastructure is being harmed by the advent of nonlinear devices, which cause harmonics to enter into the power system networks and distort the voltage and current signals. Shunt Active Power Filter (SAPF) is a new power electronics-based technology that can reduce harmonics and improve the power quality in distribution networks. This research provides an efficient and inexpensive strategy to minimizing harmonics and improving the power quality in power distribution networks by employing Shunt Active Power Filter’s (SAPF), which uses the Particle Swarm Optimized Artificial Neural Network Controller (PSO-ANN). The goal of the PSO-ANN algorithms have been developed for SAPF is to improve system performance by lowering the amount of Total Harmonic Distortion (THD). In this work, the standard PI controller is initially tuned using the PSO algorithm to obtain the optimal gain values (Ki, Kp) for the PI controller. After that, these values of the PSO-PI controller's input and output will serve as a dataset for the ANN controller. Now, the PSO algorithm is being used to tune this ANN controller in order to acquire the optimal values for the weight and bias. Using the MATLAB/SIMULINK tool, the proposed algorithm's performance is evaluated and compared to that of a PSO-PI based SAPF and the conventional PI based SAPF. The results of the simulation demonstrate that a SAPF which is based on a PSO-ANN controller is capable of achieving superior THD in the drawing source current while maintaining minimum levels and which are acceptable in accordance with the IEEE-519 standard for harmonics.
 

Index Terms—ANN-controller tuning, particle swarm optimization algorithm, PI-controller tuning, power quality, shunt active power filter, total harmonic distortion

Cite:Venkata Anjani Kumar G. and M. Damodar Reddy, "PSO Trained Feed Forward Neural Network Based SAPF for Power Quality Enhancement in Distribution Networks," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 12, No. 4, pp. 279-287, July 2023. Doi: 10.18178/ijeetc.12.4.279-287