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Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules
摘要： Defining the optimal parameters of the photovoltaic system (PV) models according to the actual real voltage and current data is a crucial process during designing, emulating, estimating, dominating, and optimizing photovoltaic systems. Therefore, it is necessary to effectively advance the optimal parameters of the models based on the proper optimization methods. For this purpose, this paper proposes an orthogonal moth flame optimization (MFO) with a local search for identifying parameters of photovoltaic cell models, which is named NMSOLMFO. The presented method is organized based on the principal exploratory and exploitative mechanisms of MFO. Also, its exploration and exploitation capability is strengthened by the orthogonal learning (OL) strategy and Nelder-Mead simplex (NMS) method, and this new scheme supports a more stable equilibrium between the central propensities. In the new MFO-based method, OL strategy can construct a healthier candidate location for the inferior agents, and then, it directs them to probe a reasonable prospective zone throughout a few rational trials. Besides, the NMS local search scheme can augment the accurateness of the global optimal solution by searching its neighborhood throughout the searching process, and the global optimum is taken as the initial point. In our study, first, the developed MFO-based approach is employed to tackle IEEE CEC 2014 benchmark cases with 30D to evaluate the effectiveness of the method in solving high dimensional and multimodal problems. Then, it is utilized to deal with parameters identification of single diode model (SDM), double diode model (DDM), and photovoltaic module model (PVM). The results and statistical studies indicate that NMSOLMFO can outperform the majority of other investigated methods concerning accuracy and convergence rapidity. The obtained results imply that the novel approach can provide a new practical tool for parameter definition in PV models, and it can be beneficial to upgrade the PV systems.
关键词： Parameter identification,Orthogonal learning,Simplex method,Moth flame optimization,Solar module
更新于2025-09-23 15:21:01
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Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization
摘要： Photovoltaic (PV) models’ parameter extraction with the tested current-voltage values is vital for the optimization, control, and evaluation of the PV systems. To reliably and accurately extract their parameters, this paper presents one improved moths-flames optimization (IMFO) method. In the IMFO, a double flames generation (DFG) strategy is proposed to generate two different types of target flames for guiding the flying of moths. Furthermore, two different update strategies are developed for updating the positions of moths. To greatly balance the exploitation and exploration, we adopt a probability to rationally select one of the two update strategies for each moth at each iteration. The proposed IMFO is used to distinguish the parameter of three test PV models including single diode model (SDM), double diode model (DDM), and PV module model (PMM). The results indicate that, compared with other well-established methods, the proposed IMFO can obtain an extremely promising performance.
关键词： moth-flame optimization,photovoltaic model,parameter extraction,double flames generation (DFG) strategy
更新于2025-09-11 14:15:04