报告详情
Adaptive Maximum Power Point Tracking Using Machine Learning for Photovoltaic Systems
编号：203
访问权限：仅限参会人
更新：2025-12-24 14:17:53 浏览：2次
拓展类型2
报告开始：暂无开始时间（Asia/Amman）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
Machine Learning (ML) technology for solar photovoltaic (PV) systems has emerged as a good option for increasing energy conversion efficiency under varying environmental conditions. This paper presents an adaptive Maximum Power Point Tracking (MPPT) approach using ML techniques to optimize real-time energy harvesting in PV systems. Traditional MPPT techniques such as Perturb and Observe (P&O) and Incremental Conductance are less efficient under rapidly varying irradiance and temperature. But the proposed ML-based MPPT scheme learns dynamically from environment and system data and forecast and optimize the operating point with a better accuracy. Various supervised learning models are compared on simulated data to identify the most accurate model with respect to accuracy, convergence speed, and computational cost. Experimental validation by MATLAB/Simulink confirms the better performance of the adaptive ML-based MPPT approach compared to conventional approaches. This paper illustrates the potential of intelligent control in improving the robustness and efficiency of PV systems in real-world applications
关键词
Machine Learning, Maximum Power Point Tracking, adaptive control, Photovoltaic (PV) Systems, Real-Time Optimization, Renewable Energy, solar energy, environmental variability, Energy Efficiency, Smart Grid Integration
报告人
Rakesh Kumar
GLA University GLA University稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
12月30日 2025
报告提交截止日期
-
12月30日 2025
注册截止日期
-
12月31日 2025
初稿截稿日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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