Support Vector Machine for Photovoltaic System Efficiency Improvement

被引：10

作者：

Takruri, Maen ^{[1
]}

Farhat, Maissa ^{[1
]}

Sunil, Sumith ^{[1
]}

Ramos-Hernanz, Jose A. ^{[2
]}

Barambones, Oscar ^{[3
]}

机构：

[1] Amer Univ Ras Al Khaimah, Dept Elect Elect & Commun Engn, Amer Univ Al Khaimah Rd, Ras Al Khaymah, U Arab Emirates

[2] Univ Basque Country, Dept Elect Engn, Barrio Sarriena S-N, Lejona 48940, Vizcaya, Spain

[3] Univ Basque Country, Dept Syst & Automat Engn, Barrio Sarriena S-N, Lejona 48940, Vizcaya, Spain

来源：

JOURNAL OF SUSTAINABLE DEVELOPMENT OF ENERGY WATER AND ENVIRONMENT SYSTEMS-JSDEWES | 2020年 / 8卷 / 03期

关键词：

Photovoltaic panel; Maximum power point estimation; Efficiency; Support vector regression; Machine learning; ARTIFICIAL NEURAL-NETWORKS; POWER-POINT TRACKING; SOLAR; OPTIMIZATION; PREDICTION; ALGORITHM; MODEL;

D O I：

10.13044/j.sdewes.d7.0275

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Photovoltaic panels are promising source for renewable energy. They serve as a clean source of electricity by converting the radiation coming from the sun to electric energy. However, the amount of energy produced by the photovoltaic panels is dependent on many variables including the irradiation and the ambient temperature, leading to nonlinear characteristics. Finding the optimal operating point in the photovoltaic characteristic curve and operating the photovoltaic panels at that point ensures improved system efficiency. This paper introduces a unique method to improve the efficiency of the photovoltaic panel using Support Vector Machines. The dataset, which is obtained from a real photovoltaic setup in Spain, include temperature, radiation, output current, voltage and power for a period of one year. The results obtained show that the system is capable of accurately driving the photovoltaic panel to produce optimal output power for a given temperature and irradiation levels.

引用

页码：441 / 451

页数：11

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