Parameters Identification of Photovoltaic Solar Cells and Modules Using Adaptive Flexible Particle Swarm Optimization Algorithm with Sequential Quadratic Programming

In this paper a new method is proposed to identify parameters of the photovoltaic (PV) solar cells model. The method is a combination of a heuristic algorithm called flexible improved particle swarm optimization (FIPSO) and a gradient based algorithm called sequential quadratic programming (SQP). PSO is known as rapidly convergent algorithm to a near optimum solution, but very slow around global optimum. Conversely, SQP is weak to escape from local optimum, but can achieve faster and more accurate convergence around the global optimum. Therefore, by gaining benefits of both algorithms, at the beginning of the search process, FIPSO is employed to find near optimum solution. then, it is switched to SQP to accelerate the search process finding more accurate solution. To validate the performance of the proposed technique, it is used to identify parameters of a single diode, double diode model, PV model and an actual PV module. Results are compared with those obtained by some conventional methods. Simulation results verify the superiority of the proposed FIPSO-SQP method to the other methods in terms of the convergence time and accuracy. In conclusion, the convergence time is significantly found reduced whilst the best result is achieved with higher precision by FIPSO-SQP