Parameter extraction of photovoltaic cell based on a multi-objective approach using nondominated sorting cuckoo search optimization

The extraction of PV cell and module parameters is vital for the accurate simulation of PV systems. PV cell exhibits non-linear nature of I–V and P–V curves. Hence, parameters estimated at the optimum value of the current error criterion, such as root mean square error (RMSE) of current, may not give accurate power estimation at maximum power point (MPP). In this work, the shortcomings of the single objective optimization (SOO) approach based parameter estimation methods are assessed using two different problem formulations: compound objective function and weighted sum. The results from the SOO approach indicate that weightage assigned to objectives such as RMSE and the percentage relative power Error at MPP (% RPE) has impacted the accuracy of parameter estimation. The selection of weightage for each objective is indeed a difficult choice to make. Moreover, the obtained cumulative value of RMSE and % RPE is high. In order to overcome these shortcomings of the SOO approach, a multi-objective optimization (MOO) approach based parameter estimation is formulated. Then formulated MOO problem is solved by using the proposed nondominated sorting cuckoo search optimization (NSCSO). Combining nondominated sorting, a crowded comparison technique, and Lévy flight characteristics, the NSCSO algorithm is proposed, which is inspired by both the NSGA-II and NSCS algorithms. Results indicate that the proposed parameter estimation method based on the MOO approach using NSCSO has given the optimum value for cumulative RMSE and % RPE in comparison with the SOO approach using the CSO algorithm. The proposed method is also validated on various PV models: PVM 752 (GaAs thin film) cell, STM6-40 (monocrystalline), and STP6-120/36 (polycrystalline) silicon modules.