Optimization controller design of CACZVS three phase PFC converter using particle swarm optimization

Abstract

The compound active clamp soft switching threephase power factor correction converter, as an improved threephase PWM converter topology, has the advantages of high efficiency, high power factor, soft switching of all switches and so on. Its traditional control system configuration is the double closed loop control scheme with the current loop as inner loop and the voltage loop as outer loop. As a multivariable and strong coupling nonlinear system, the controller design of the three phase PWM converter, either the classical PID control method or the nonlinear control method, involves the time consuming controller parameters tuning problem. It is even worse that the improperly adjusted controller parameters might damage the system. In this paper, the particle swarm optimization is proposed to optimize the control parameters of the double closed loop PI controllers. To deal with this multi-parameter multiobjective optimization problem, we combine the PSO algorithm with the Pareto optimal solution theory to optimize the PI control parameters. The concrete steps are explained in this paper, and the effectiveness of proposed optimization method is verified by both the simulation and experiment results.

An Optimized PI Controller Design for Three Phase PFC Converters Based on Multi-Objective Chaotic Particle Swarm Optimization

“…The results shown in Table II are the average values for running the optimization algorithm 50 times, where k r denotes the average iteration times used for the different algorithms to reach stable optimized solutions. As can be seen from Table II, the traditional multi-objective PSO method in [14] (using the random initial particles) makes it easy to find a local optima because of the non-uniform distribution of the particles. Obviously, the proposed method has the highest average objective function and the fewest average iterations.…”

“…In this paper, the proposed chaotic initialization method has the advantage of global convergence. In order to prove this, based on the same multi-objective PSO condition above but with different particles initialization methods, the comparison results using the random number in [14], the logistic map in [16], and the proposed chaotic map are shown in Table II. The results shown in Table II are the average values for running the optimization algorithm 50 times, where k r denotes the average iteration times used for the different algorithms to reach stable optimized solutions.…”

“…This objective is denoted as J v2 given by Eq. (14). The objective of the current loop is to achieve a unity power factor.…”

“…In the case of the PSO method, a non-uniform distribution of the initial particles decreases the global convergence performance. To deal with the first problem, reference [14] proposed using the PSO method and the Pareto optimal solution theory to overcome the difficulty of weights selection. For the second problem, because the uniform distribution of the initial particles in the solution space accelerates the convergence speed and reduces the possibility of falling into a local optimum [15], references [16], [17] use chaotic maps to produce the initial particles, where the ergodic characteristic of the chaotic maps was used to get uniform distribution.…”

“…However, the difficulty of controller parameters tuning limits, to a certain extent, the performance of these control algorithms in practical applications. To avoid the above difficulty, fuzzy logic [11], genetic algorithm (GA) [12], and particle swarm optimization (PSO) theory [13], [14] have been applied to controller design and parameters optimization. However, the control parameter optimization of three phase PWM converters Manuscript received Jul.…”