期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 11, 期 3, 页码 1309-1322出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2019.2923732
关键词
Splines (mathematics); Maximum power point trackers; Convergence; Data models; Photovoltaic systems; Particle swarm optimization; Heuristic search; data-driven; maximum power point tracking; partial shading conditions; photovoltaics systems
资金
- Hong Kong RGC General Research Fund Project [11215418]
- Hong Kong RGC Theme-based Research Scheme Project [T32-101/15-R]
- Fundamental Research Funds for the Central Universities [06500103, 06500078]
- National Nature Science Foundation of China [U1836106]
- Foundation of Key Laboratory of Wind Energy and Solar Energy Technology, Ministry of Education [2018ZD02]
- Open Research Subject of Key Laboratory of Fluid and Power Machinery at Xihua Universtiy, Ministry of Education [SZJJ2019-011]
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.
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