An optimizing MPPT method based on IPSO for PV model under partial shading condition

The natural conditions such as the solar irradiance intensity, temperature, cloudiness, etc, have brought about periodicity, randomness and uncertainty in photovoltaic (PV) performance characteristic. However, up to now, few studies focus on the integration of multiple factors. In addition, in the face of large-scale distributed photovoltaic array simulation, the high-efficiency computation n of scaled photovoltaics has not been deeply demonstrated. Therefore, this research optimizes the parameters of PV module and studies the relationship between irradiance intensity, temperature and the generation of photovoltaic. What’s more, on account of the PV array P-U characteristic curve appears repeatedly to change single-peak into multi-peaks under partial shading conditions which may cause the traditional maximum power point tracking (MPPT) algorithm fails to track the peak. An equivalent model for large-scale power generation with improved particle swarm optimization (IPSO) algorithm is proposed. Compared with traditional PSO algorithm , the IPSO method prevents the MPPT trapping in local maximum power point and finding the global maximum power point. Also IPSO accelerates the convergence speed and improves the algorithm stability by using the adaptive acceleration coefficients and inertia weight. The feasibility of this algorithm is verified based on Matlab simulation.