An improved optimization technique for estimation of solar photovoltaic parameters

M. Derick, C. Rani*, M. Rajesh, M.E. Farrag, Y. Wang, K. Busawon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)
142 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)116–124
Number of pages9
JournalSolar Energy
Early online date10 Aug 2017
Publication statusPublished - 15 Nov 2017


  • genetic algorithm
  • wind driven optimization
  • artificial bee swarm optimization


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