An enhanced neural network algorithm and its applications for numerical optimization and parameter extraction of photovoltaic models
Aining Chi, Seyedali Mirjalili, Yiying Zhang
TL;DR
This paper introduces an enhanced neural network algorithm for optimizing photovoltaic models and shows it outperforms existing methods in parameter extraction.
Contribution
The novel ENNA algorithm uses a new transfer operator with three learning strategies for improved optimization performance.
Findings
ENNA achieved root mean square errors of 0.00098602, 0.000982485, and 0.00242507 for three PV models.
ENNA outperformed 10 metaheuristic algorithms in numerical comparisons and convergence performance.
The algorithm was tested on 52 benchmark functions and showed strong optimization capabilities.
Abstract
Solar energy is a clean energy source with great application prospects. Photovoltaic (PV) system plays a very important role in converting solar energy into electricity. Optimizing, controlling, and simulating the PV system is of great significance for improving the conversion efficiency of solar energy. The key lies in how to extract the unknown parameters of the PV model. To address this issue, this paper proposes an enhanced neural network algorithm (ENNA). In ENNA, a new transfer operator with three learning strategies based on the defined perturbation operator and elite operator is designed, which makes full use of the obtained population information, including the optimal position of the population, the mean position of the population, and the historical population. To verify the performance of ENNA, ENNA is first used to solve 52 complex benchmark functions. Then, ENNA is…
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Taxonomy
TopicsPhotovoltaic System Optimization Techniques · Solar Radiation and Photovoltaics · Hybrid Renewable Energy Systems