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Parameter Identification for Photovoltaic Models Using an Improved Learning Search Algorithm
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DOI：10.1109/ACCESS.2020.3003814.png)
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Abstract
En 中文
As a renewable energy resource, solar photovoltaics (PV's) possess a promising future. Thus, it is important to simulate, evaluate and control the PV systems. To identify the parameters for the photovoltaic (PV) models, an improved learning search optimization algorithm (ILSA) is proposed in this paper. The proposed ILSA has the following three key features: (i) the constant self-adjustment rate changes with the iteration. (ii) a new part with a self-adaptive weighting of the current best and worst solution is introduced to learning patterns to guide the iterative direction. (iii) a perturbation method is added to avoid the algorithm falling into local optimum. In order to assess the effectiveness of ILSA relative to other state-of-the-art algorithms, single diode, double diode, and PV model are used for test. Our experimental results reveal that the ILSA performs well in terms of the accuracy of optimization solutions and effectiveness.
Keywords:
Learning search algorithm
parameter identification
photovoltaic model
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