Affiliations: [a] School of Artificial Intelligence, Wuchang University of Technology, Wuhan, China
| [b] School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, China
| [c]
Sichuan Vocational and Technical College of Communications, Chengdu, China
| [d]
Zhejiang Electronic Information Products Inspection and Research Institute (Key Laboratory of Information Security of Zhejiang Province)
Correspondence:
[*]
Corresponding author. Ruoli Tang, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, China. E-mail: [email protected] and Jun Zhang, Zhejiang Electronic Information Products Inspection and Research Institute (Key Laboratory of Information Security of Zhejiang Province) E-mail: [email protected].
Abstract: 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.
Keywords: Building integrated photovoltaic system, maximum power point tracking, PV configuration, intelligent control, extreme learning machine
