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Applied energy v.212, 2018년, pp.1578 - 1588   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation

Chen, Xu (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China ) ; Xu, Bin (School of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China ) ; Mei, Congli (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China ) ; Ding, Yuhan (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China ) ; Li, Kangji (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China ) ;
  • 초록  

    Abstract Parameters estimation of photovoltaic (PV) model based on experimental data plays an important role in the simulation, evaluation, control, and optimization of PV systems. In the past decade, many metaheuristic algorithms have been used to extract the PV parameters; however, developing hybrid algorithms based on two or more metaheuristic algorithms may further improve the accuracy and reliability of single metaheuristic algorithms. In this paper, by combining teaching-learning-based optimization (TLBO) and artificial bee colony (ABC), we propose a new hybrid teaching-learning-based artificial bee colony (TLABC) for the solar PV parameter estimation problems. The proposed TLABC employs three hybrid search phases, namely teaching-based employed bee phase, learning-based on looker bee phase, and generalized oppositional scout bee phase to efficiently search the optimization parameters. TLABC is applied to identify parameters of different PV models, including single diode, double diode, and PV module, and the results of TLABC are compared with well-established TLBO and ABC algorithms, as well as those results reported in the literature. Experimental results show that TLABC can achieve superior performance in terms of accuracy and reliability for different PV parameter estimation problems. Highlights Teaching-learning-based artificial bee colony algorithm is proposed. Three hybrid teaching-learning-based bee search phases are presented. The method is applied to solve three photovoltaic parameters estimation problems. It achievesvery competitive results in terms of accuracy and reliability.


  • 주제어

    Photovoltaic parameter estimation .   Metaheuristic algorithm .   Teaching-learning-based optimization .   Artificial bee colony .   Hybridization.  

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