An improved optimization technique for estimation of solar photovoltaic parameters. (15th November 2017)

• Record Type: Journal Article
• Authors: Derick, M.; Rani, C.; Rajesh, M.; Farrag, M.E.; Wang, Y.; Busawon, K.
• Is Part Of: Solar energy. Volume 157(2017)
• Page Start: 116
• Page End: 124
• Publication Date: 2017-11-15
• Subjects: Double diode model -- Genetic algorithm -- Pattern search -- Simulated annealing -- Wind driven optimization; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2017.08.006
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Wind driven optimization is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is verified. The parameters of single diode and double diode models of solar PV is estimated. The results compared with results obtained through Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA). The results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Abstract: The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV.