MPPT perturbation optimization of photovoltaic power systems based on solar irradiance data classification

Yan, Ke, Du, Yang, and Ren, Zixiao (2019) MPPT perturbation optimization of photovoltaic power systems based on solar irradiance data classification. IEEE Transactions on Sustainable Energy, 10 (2). pp. 514-521.

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The tracking accuracy and speed are two main issues for the fixed step perturb-and-observe maximum power point tracking (MPPT) method. This study proposes a novel solution to balance the tradeoff between performance and cost of the MPPT method. The perturbation step size is determined off-line for a specific location based on the local irradiance data. The support vector machine is employed to automatically classify the desert or coastal locations using historical irradiance data. The perturbation step size is optimized for better system performance without increasing the control complexity. Simulations and experiments have been carried out to verify the effectiveness and superiority of the proposed method over existing approaches. The experimental results show a 5.8% energy generation increment by selecting optimal step sizes for different irradiance data types.

Item ID: 58368
Item Type: Article (Research - C1)
ISSN: 1949-3037
Keywords: Support vector machines, Sea measurements, Perturbation methods, Maximum power point trackers, Testing, Training, Clouds
Copyright Information: © 2018 IEEE.
Additional Information:

A previous version of this paper has been published in 2015 IEEE 16th Workshop on Control and Modeling for Power Electronics (COMPEL) with title “Perturbation optimization of maximum power point tracking of photovoltaic power systems based on practical solar irradiance data.”

Funders: National Science Foundation of China (NSFC), Xi'an Jiaotong-Liverpool University (XJLU), Jiangsu University (JU) S&T Programme
Projects and Grants: NSFC Grant 61602431, XJLU Grant RDF-15-01-40, JU S&T Programme Grant 17KJB470012
Date Deposited: 19 Jun 2020 06:24
FoR Codes: 40 ENGINEERING > 4008 Electrical engineering > 400803 Electrical energy generation (incl. renewables, excl. photovoltaics) @ 60%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 40%
SEO Codes: 85 ENERGY > 8505 Renewable Energy > 850504 Solar-Photovoltaic Energy @ 100%
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