Accelerating Parameter Estimation for Photovoltaic Models via Parallel Particle Swarm Optimization

Bio-inspired metaheuristic algorithms have been widely proposed to estimate parameters of photovoltaic (PV) models in recent years due to its ability to handle nonlinear functions regardless of the derivatives information. However, these algorithms normally utilize multiple agents/particles in the search process, and it takes much time to search the possible solutions in the whole search domain by sequential computing devices. This paper proposes parallel particle swarm optimization (PPSO) method to extract and estimate the parameters of a PV model. The algorithm is implemented in OpenCL and is executed on Nvidia multi-core GPUs. From the simulation results, it is observed that the proposed method is capable of accelerating the computational speed with the same accuracy in comparison to sequential particle swarm optimization (PSO).