Solar cells performance testing and modeling based on particle swarm algorithm

Existing solar cells performance testing and modeling algorithms possess several drawbacks such as high complexity, low measuring accuracies and poor robustness to the small change of operating condition. A new approach is proposed to solve these problems. Firstly, the method introduced a series of semiempirical formula to separate and quantify the influence of all significant factors. Secondly, a chaos particle swarm optimization algorithm (CPSO) was used for extracting model parameters, in which the global search performance and local convergence of particle swarm optimization (PSO) were improved by the proposed chaotic search strategy. The application results of solar cells I-V characteristics test and measurement system demonstrate that the measured data and the calculated data, where the performance model parameters derived from the approach have been employed, represent conformity excellently.