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Maximum power tracking method for photovoltaic power generation based on boosting algorithm
被引：0
作者：
Ye, Ye
[1
]
Xu, Danlu
[2
]
Bao, Jieli
[1
]
机构：
[1] Taizhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Zhejiang, Taizhou,318000, China
[2] State Grid Zhejiang Electric Power Co., Ltd., Zhejiang, Hangzhou,310051, China
关键词：
Boosting algorithm;
Extreme learning machine;
Integration classification;
Light intensity;
Maximum power tracking;
Photovoltaic power generation;
D O I：
10.1186/s40807-025-00159-x
中图分类号：
学科分类号：
摘要：
The maximum power tracking of photovoltaic power generation is a critical component in enhancing power generation efficiency. To improve the efficiency of photovoltaic power generation, this study investigates a maximum power tracking method for photovoltaic power generation based on the boosting algorithm. The maximum power point of photovoltaic power generation is determined based on the relationship between the maximum power output point, input voltage, and input resistance. The data of light intensity, temperature, photovoltaic cell voltage, and current are set as inputs for the boosting algorithm. An extreme learning machine is selected as the base classifier for the Boosting algorithm, and use a self-iterative weighted ensemble classification algorithm as the strong learning classifier for the Boosting algorithm, and a self-iterative weighted ensemble classification algorithm is utilized as the strong learning classifier for the Boosting algorithm. Subsequently, the detection result of the maximum power point of photovoltaic power generation is output. Based on the detection results of the maximum power point of photovoltaic power generation, the sliding mode layer extreme value search control method is employed to adjust the output voltage of the photovoltaic array to the optimal value, thereby achieving maximum power tracking of photovoltaic power generation. The experimental results demonstrate that this method can accurately track the maximum power of photovoltaic power generation, with a maximum power tracking efficiency exceeding 99%. © The Author(s) 2025.
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