An Online Data-Driven Hyperbolic Assumption Global Maximum Power Point Tracking Method for PV Systems Under Partial Shading Conditions

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Abstract
To address the issue of efficiency degradation and output power oscillation of photovoltaic (PV) arrays under partial shading conditions (PSCs), this article proposes a global maximum power point tracking (GMPPT) method based on an online data-driven hyperbolic function model. The proposed method utilizes real-time sampled data to establish a curve-fitting model for PV output estimation. By employing adaptive weighting factor, the online fitting model is dynamically combined with the conventional perturbation and observation (P&O) method. Compared to existing GMPPT algorithms, the proposed method achieves fast tracking speed and effectively narrows the search range, reducing the voltage trajectory during the search process. Therefore, the proposed GMPPT method is capable of adapting to complex PSCs with frequent environmental changes, thereby enhancing the overall performance of PV systems. The effectiveness of the proposed GMPPT method is validated through comparative experiments with existing approaches.
Funding Information
  • National Natural Science Foundation of China (51977145)

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