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A memory and update strategy-based social group optimization for unknown parameter identification of photovoltaic modules

algorithms-and-analysis-of-algorithms
computer-education
data-mining-and-machine-learning
optimization-theory-and-computation

Abstract

With the increasingly severe environmental issues caused by fossil fuel consumption, clean energy technology represented by photovoltaics (PV) has attracted increasing research attention. However, the unknown parameter configuration of PV devices is related to conditions such as temperature and irradiance of the environment where the equipment is located, and existing methods often suffer from limited accuracy and reliability of parameter identification. To address these challenges, in this paper, a Memory and Update Strategy-based Social Group Optimization (MUS-SGO) algorithm for PV parameter identification is proposed, aiming to enhance both accuracy and robustness. To strengthen local exploitation capability, a dynamic memory-guided strategy is employed. This strategy constructs a historical memory repository to store high-quality historical solutions and, together with the dynamic memory weights, guides MUS-SGO toward historical optimal regions. To maintain population diversity and accelerate convergence, an adaptive population update strategy is applied, which adaptively replaces a proportion of low-fitness individuals with new ones depending on the stage of MUS-SGO. Comparative experiments are conducted on the poly-crystalline KC200GT dataset under varying temperature and irradiance, using seven representative algorithms as baselines. The results demonstrate that MUS-SGO achieves a smaller root mean square error (RMSE) than the compared algorithms, with r2 values close to 1.0. This indicates that MUS-SGO ensures both high accuracy and strong robustness for PV parameter identification.
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