Advanced design for nonlinear photovoltaic system problems: A co-evolutionary framework based on a decomposition approach

Abstract

Under complex outdoor environments, accurately estimating the unknown parameters of nonlinear photovoltaic (PV) systems remains a major challenge. Key parameters are often influenced by changing weather conditions such as temperature and irradiance. Although many approaches have been proposed, their reliability often drops when environments shift or computing resources are limited. To address these issues, this paper proposes a knowledge transfer-driven self-adaptive decomposition multi-problem cooperative co-evolutionary framework, named SaCEPV, for parameter estimation. SaCEPV is designed to solve a group of related problems simultaneously, where each problem corresponds to parameter estimation for PV modules under specific temperature and irradiance settings. First, the framework integrates a self-adaptive parameter method that dynamically controls the search behavior. Furthermore, knowledge transfer mechanism based on population dynamic diversity is introduced, which adaptively determines when and what to transfer among related problems by analyzing population evolution characteristics. This mechanism enables effective knowledge sharing across correlated problems. Moreover, to handle the complexity of nonlinear PV models, the framework incorporates a parameter pre-decomposition method that separates model components into linear and nonlinear subcomponents based on the nature of the unknown parameters. Then different estimation strategies are then applied to each component accordingly. To evaluate the effectiveness of SaCEPV, the first multi-problem test suite is constructed for PV parameter estimation, covering multiple PV models under various environmental conditions. Experimental results show that SaCEPV achieves superior accuracy and robustness across all problem instances, highlighting strong potential for real-world PV modeling in diverse scenarios.