Adaptive Harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction. (March 2022)

• Record Type: Journal Article
• Authors: Song, Shiming; Wang, Pengjun; Heidari, Ali Asghar; Zhao, Xuehua; Chen, Huiling
• Is Part Of: Engineering applications of artificial intelligence. Volume 109(2022)
• Publication Date: 2022-03
• Subjects: Parameter extraction -- Harris hawks optimization -- Persistent trigonometric differences -- PV cells -- Photovoltaic; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104608
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, an adaptive Harris hawk optimization with persistent trigonometric (sine–cosine)-differences (ADHHO) is proposed for parameter identification of Photovoltaic (PV) systems. In the optimized version of HHO, we innovatively propose the persistent-trigonometric-differences mechanism for improving the global search capability of HHO; moreover, we improve the energy factor in the original algorithm so that ADHHO obtains a better balance between exploration and exploitation. Note that the proposed method can obtain lower CPU time in parameter extraction for the three-diode and PV module models with an enhanced parameter extraction performance. To validate the performance of ADHHO, we verified the parameter extraction capability of the single-diode model (SDM), double-diode model (DDM), triple-diode model (TDM), and PV module model (PVM), respectively. Further, we verified the parameter extraction effect of ADHHO in three commercial cells with different light intensity and temperature conditions. Experiments show that the method proposed in this paper can reasonably simulate the output performance of solar PV cells and can be used as a trustworthy method for the extraction of unknown parameters of solar PV systems. Highlights: A proposed method is reinforced based on Harris Hawk optimization algorithm (HHO). A novel mechanism called Persistent sine cosine differences (PSCD) is proposed. A dramatic improvement of the performance of HHO's optimal search is achieved. The proposed method is used to extract the parameters of four solar systems. The efficiency and accuracy of ADHHO are demonstrated to be highly advantageous.