An optimized proportional resonant current controller based genetic algorithm for enhancing shunt active power filter performance

Abstract

Current harmonics produced by nonlinear loads have destructive effects that affect the power quality of the power system and cause serious problems throughout the network. Active Power Filter (APF) is one of the best and most common solutions to reduce or eliminate harmonic disturbances; But due to the complexity and sensitivity of these filters, the accurate and fast performance of the control technologies used in it is the important and basic requirement. Therefore, by performing accurate and optimal adjustments for APF control, system performance and power quality level can be improved significantly. In this article, the design principles of Shunt Active Power Filter (SAPF) are introduced and the optimal control method of this system is explained by providing appropriate modeling. Also, as a suitable structure for the current control of the SAPF system, a Proportional-Resonant (PR) controller optimized by Genetic Algorithm (GA) is proposed. The PR controller with the capability of accurate tracking of harmonic currents and fast dynamic response is highly compatible with the SAPF system. In general, the goal is to achieve better and more appropriate performance to reduce harmonic disturbances and create a robust, faster, more accurate, flexible, stable, and optimal performance of these systems in detecting and generating compensation signals. The simulations have been done in MATLAB/SIMULINK, and also the experimental tests of this device have been done and validated in a real DSP-based SAPF. Finally, according to the results obtained under steady-state conditions with a favorable dynamic response, the Total Harmonic Distortion (THD) of the grid current has been significantly reduced from 20.2 % to 4.2 % for the R-L load and from 56.1 % to 4.5 % for the R-C load with a power factor of almost unity. These results confirm the optimal and standard performance of the proposed method.