Parameter extraction of solar photovoltaic modules using penalty-based differential evolution

This paper proposes a penalty based differential evolution (P-DE) for extracting the parameters of solar photovoltaic (PV) modules at different environmental conditions. The two diode model of a solar cell is used as the basis for the extraction problem. The analyses carried out using synthetic current–voltage (I–V) data set showed that the proposed P-DE outperforms other Evolutionary Algorithm methods, namely the simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO). P-DE consistently converges to the global optimum values very rapidly. The performances are evaluated using the well known quality test and student T-tests. Furthermore, the P-DE extraction method is practically validated by six solar modules of different types (multi-crystalline, mono-crystalline, and thin-film). The results were found to be in close agreement with the experimental I–V data set, especially at very low irradiance values. The latter can be very useful in predicting the performance of the solar system under partial shading conditions. The main application of the proposed work is the possibility of developing a highly accurate simulator for solar PV system designer.