Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution

Abstract Photovoltaic (PV) system as a vital element in the utilize of solar energy, its optimization, control, and simulation are significant. The performance of the PV system is mainly influenced by its model parameters that are varying and unavailable, thus identifying these model parameters is always desired. However, accurate and robust parameters estimation of PV models brings great challenges to the existing methods, since the complicated characteristics when estimating the parameters. Hence, to efficiently provide accurate parameters for the PV model, this study develops a self-adaptive ensemble-based differential evolution algorithm. Three different mutation strategies with different properties are combined into two groups for updating each individual. Furthermore, in order to make the best of different mutation strategies, a self-adaptive scheme is suggested to equilibrate population diversity and convergence, by adjusting the proportion of the mutation strategies used in the population. To evaluate the performance of SEDE, it is used to obtain the parameters of three PV models and compared with other well-established algorithms. Systematic comparison results indicate that SEDE is capable of estimating the model parameters with higher efficiency.

[1]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[2]  Hany M. Hasanien,et al.  Parameter Estimation of Three Diode Photovoltaic Model Using Grasshopper Optimization Algorithm , 2020, Energies.

[3]  Diego Oliva,et al.  Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm , 2017 .

[4]  D. Kler,et al.  A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer , 2019, Energy Conversion and Management.

[5]  Krishna Busawon,et al.  Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters , 2018, IEEE Journal of Photovoltaics.

[6]  Heng Wang,et al.  Parameter extraction of solar cell models using improved shuffled complex evolution algorithm , 2018 .

[7]  A. K. Al-Othman,et al.  Simulated Annealing algorithm for photovoltaic parameters identification , 2012 .

[8]  Jing Liang,et al.  Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models , 2018, Applied Energy.

[9]  Wenxiang Zhao,et al.  Parameters identification of solar cell models using generalized oppositional teaching learning based optimization , 2016 .

[10]  Souad Chebbi,et al.  Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches , 2018, Renewable and Sustainable Energy Reviews.

[11]  Yong Wang,et al.  Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm , 2017 .

[12]  Ahmed Fathy,et al.  Parameter estimation of photovoltaic system using imperialist competitive algorithm , 2017 .

[13]  N. Rajasekar,et al.  Bacterial Foraging Algorithm based solar PV parameter estimation , 2013 .

[14]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.

[15]  Xuesong Yan,et al.  Parameter estimation of photovoltaic models with memetic adaptive differential evolution , 2019, Solar Energy.

[16]  Huaglory Tianfield,et al.  Biogeography-based learning particle swarm optimization , 2016, Soft Computing.

[17]  Kang Li,et al.  An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models , 2014 .

[18]  Kanungo Barada Mohanty,et al.  Parameter estimation of single diode PV module based on GWO algorithm , 2019, Renewable Energy Focus.

[19]  Fei Yu,et al.  A multi-role based differential evolution , 2019, Swarm Evol. Comput..

[20]  Douglas H. Werner,et al.  The Wind Driven Optimization Technique and its Application in Electromagnetics , 2013, IEEE Transactions on Antennas and Propagation.

[21]  N. Rajasekar,et al.  Solar PV Modelling and Parameter Extraction Using Artificial Immune System , 2015 .

[22]  Alireza Rezazadeh,et al.  Parameter identification for solar cell models using harmony search-based algorithms , 2012 .

[23]  Guohua Wu,et al.  Differential evolution with multi-population based ensemble of mutation strategies , 2016, Inf. Sci..

[24]  A. Rezaee Jordehi,et al.  Parameter estimation of solar photovoltaic (PV) cells: A review , 2016 .

[25]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[26]  Meie Shen,et al.  Differential Evolution With Two-Level Parameter Adaptation , 2014, IEEE Transactions on Cybernetics.

[27]  Xu Chen,et al.  An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models , 2019, Energy Conversion and Management.

[28]  Wei Han,et al.  Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm , 2014, TheScientificWorldJournal.

[29]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[30]  T. Easwarakhanthan,et al.  Nonlinear Minimization Algorithm for Determining the Solar Cell Parameters with Microcomputers , 1986 .

[31]  Yi-Ming Wei,et al.  An adaptive hybrid model for day-ahead photovoltaic output power prediction , 2020 .

[32]  Dhiaa Halboot Muhsen,et al.  Parameters extraction of double diode photovoltaic module’s model based on hybrid evolutionary algorithm , 2015 .

[33]  Xuehua Zhao,et al.  Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts , 2020 .

[34]  Xin Wang,et al.  Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization , 2017 .

[35]  Mojtaba Alizadeh,et al.  Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm , 2019, Solar Energy.

[36]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[37]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[38]  Jing J. Liang,et al.  Classified perturbation mutation based particle swarm optimization algorithm for parameters extraction of photovoltaic models , 2020 .

[39]  Jiangang Yao,et al.  A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models , 2018 .

[40]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[41]  Sílvio Mariano,et al.  Collaborative swarm intelligence to estimate PV parameters , 2019, Energy Conversion and Management.

[42]  Xu Chen,et al.  A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module , 2019, Applied Energy.

[43]  A. Sellami,et al.  Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction , 2010 .

[44]  Xu Chen,et al.  Parameters identification of photovoltaic models using an improved JAYA optimization algorithm , 2017 .

[45]  Tao Yu,et al.  Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition , 2019, Journal of Cleaner Production.

[46]  Ming Xu,et al.  A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models , 2020 .

[47]  Jing J. Liang,et al.  Evolutionary multi-task optimization for parameters extraction of photovoltaic models , 2020 .

[48]  Guohua Wu,et al.  Ensemble strategies for population-based optimization algorithms - A survey , 2019, Swarm Evol. Comput..

[49]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[50]  Nadarajah Kannan,et al.  Solar energy for future world: - A review , 2016 .

[51]  Lei Guo,et al.  Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm , 2016 .