Comparative Performance of UPQC Control System Based on PI-GWO, Fractional Order Controllers, and Reinforcement Learning Agent

Abstract

In this paper, based on a benchmark on the performance of a Unified Power Quality Conditioner (UPQC), the improvement of this performance is presented comparatively by using Proportional Integrator (PI)-type controllers optimized by a Grey Wolf Optimization (GWO) computational intelligence method, fractional order (FO)-type controllers based on differential and integral fractional calculus, and a PI-type controller in tandem with a Reinforcement Learning—Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) agent. The main components of the UPQC are a series active filter and an Active Parallel Filter (APF) coupled to a common DC intermediate circuit. The active series filter provides the voltage reference for the APF, which in turn corrects both the harmonic content introduced by the load and the VDC voltage in the DC intermediate circuit. The UPQC performance is improved by using the types of controllers listed above in the APF structure. The main performance indicators of the UPQC-APF control system for the controllers listed above are: stationary error, voltage ripple, and fractal dimension (DF) of the VDC voltage in the DC intermediate circuit. Results are also presented on the improvement of both current and voltage Total harmonic distortion (THD) in the case of, respectively, a linear and nonlinear load highly polluting in terms of harmonic content. Numerical simulations performed in a MATLAB/Simulink environment demonstrate superior performance of UPQC-APF control system when using PI with RL-TD3 agent and FO-type controller compared to classical PI controllers.