A Prediction-Guided and Local-Execution Collaborative MPPT Method for Dynamic Environments: Mechanism Insights and Performance Validation

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Dynamic irradiance variations, temperature disturbances, and partial shading conditions continuously shift the maximum power point (MPP) of photovoltaic (PV) systems, increasing the difficulty of maximum power point tracking (MPPT). Conventional MPPT methods mainly rely on real-time feedback and lack the ability to perceive MPP migration trends under dynamic environments, while existing intelligent MPPT studies primarily focus on prediction accuracy rather than the effective integration of predictive information into control processes. To address this issue, a collaborative MPPT method combining gated recurrent unit (GRU)-based prediction guidance and incremental conductance (INC)-based local execution is proposed. The GRU model provides operating-point trend information, while the INC algorithm ensures feedback-based regulation and stable convergence. The proposed method is evaluated under dynamic irradiance variations, temperature disturbances, partial shading conditions, and composite dynamic conditions. Results show that the GRU–INC framework achieves higher tracking efficiency, lower tracking error, and more stable operating trajectories than the conventional INC-based approach, particularly under partial shading and composite dynamic conditions. Ablation studies further demonstrate that the performance improvement mainly originates from the collaboration between prediction guidance and real-time feedback rather than from the prediction module alone. The results confirm the effectiveness of the proposed collaborative mechanism for MPPT under dynamic environments and highlight the benefit of integrating prediction guidance with feedback regulation

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