Today, the need for photovoltaic (PV) energy is increasing due to its abundance and hazard-free nature. A PV system is described by its mathematical models. These models contain intrinsic parameters, not provided by the manufacturer. Therefore, accurately estimating these parameters is crucial for improving the reliability and efficiency of PV systems. However, the nature of PV system is highly non-linear thus finding these parameters is quite challenging. Several conventional optimization approaches have been applied but they suffer from premature convergence and stagnation. Although Quantum behaved Particle Swarm Optimization (QPSO) has been utilized for many optimization problems, its strengths and weaknesses in the context of PV parameters estimation is not explored. This paper presents an improved version of the QPSO called Modified Quantum inspired particle swarm method (MQPSO). The proposed MQPSO incorporate dual attractors, elitism strategy and local refinement thus enhancing the convergence behavior and performance. Quantitative analysis clearly shows that MQPSO outperforms conventional QPSO on all three PV models and modules. Compared with the standard QPSO, MQPSO has an average RMSE reduction of 24.74% for the SDM, 59.32% for the DDM, and 14.99% for the TDM. The results demonstrate the merit and efficiency of the proposed MQPSO as compared to other well-developed Optimization methods.
Rehman et al. (Mon,) studied this question.