最大功率点跟踪
建筑集成光伏
光伏系统
极限学习机
计算机科学
底纹
辐照度
太阳辐照度
人工智能
工程类
电压
电气工程
人工神经网络
气象学
物理
计算机图形学(图像)
量子力学
逆变器
作者
Qing An,Ruoli Tang,Hongfeng Su,Jun Zhang,Xin Li
摘要
Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy.
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