Bacterial Foraging Algorithm Guided by Particle Swarm Optimization for Parameter Identification of Photovoltaic Modules

This paper presents an optimization-based solution to the problem of offline parameter identification in crystalline silicon photovoltaic (PV) modules. An objective function representing the difference between computed and targeted performance is minimized using global heuristic optimization algorithms. The targeted performance signifies the values of four characteristics at standard test conditions (STCs), as given in the manufacturer datasheet. The optimization problem is solved with three different algorithms, i.e., particle swarm optimization (PSO), bacterial foraging (BF), and PSO-guided BF. On an LDK PV test module, the PSO-guided BF algorithm gives the best objective function value. Parameters of the test module are also identified through measured performance. The good matching between experimental measurements and computed performance of the test PV module validates the proposed technique, and shows the accuracy of modeling.