A Novel Spline Model Guided Maximum Power Point Tracking Method for Photovoltaic Systems

• Published in: IEEE transactions on sustainable energy Vol. 11; no. 3; pp. 1309 - 1322
• Main Authors: Huang, Chao, Wang, Long, Zhang, Zijun, Shun-cheung Yeung, Ryan, Bensoussan, Alain, Shu-hung Chung, Henry
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer applications; Computer simulation; Convergence; Data models; data-driven; Electric power generation; Heuristic methods; Heuristic search; Iterative methods; Maximum power point trackers; maximum power point tracking; Maximum power tracking; partial shading conditions; Particle swarm optimization; Photovoltaic cells; Photovoltaic systems; Photovoltaics; photovoltaics systems; Search process; Shading; Splines (mathematics)
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2019.2923732

This paper develops a novel data-driven maximum power point tracking (MPPT) method, which is of two-fold, to benefit the power generation of photovoltaics (PV) systems facing variable partial shading conditions (PSCs). Under each PSC, the proposed MPPT utilizes a compact data-driven modeling process to develop the power-voltage ( P - V ) curve model via the natural cubic spline. Next, the proposed MPPT method develops a novel natural cubic spline guided iterative search process to update the P - V curve model having multiple peaks and to promptly obtain the global maximum power point (GMPP) under the considered PSC. This is a pioneer study which discusses a GMPPT algorithm using a natural cubic spline-based P - V curve model. The convergence of the MPP tracked by the proposed algorithm to the GMPP is theoretically ensured by the property of the natural cubic spline. The effectiveness and robustness of the proposed algorithm have been comprehensively evaluated via extensive simulation studies and experiments. Computational results demonstrate that the proposed algorithm is more efficient and effective to attain GMPPs under variable PSCs by comparing with recent MPPT methods using heuristic techniques, which are easily trapped into local MPP under variable PSCs.