A rapid prediction model of photovoltaic power generation for autonomous long‐duration aerostat

Autor: Qianshi Liu, Guoning Xu, Zhaojie Li, Yang Gao, YanChu Yang, Yongxiang Li, Hao Du
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: IET Renewable Power Generation, Vol 17, Iss 6, Pp 1597-1608 (2023)
Druh dokumentu: article
ISSN: 1752-1424
1752-1416
DOI: 10.1049/rpg2.12697
Popis: Abstract Autonomous long‐duration aerostats (LDA) are one of the most popular research directions of high‐altitude platforms (HAPS) in recent years. Solar photovoltaic (PV) array is the energy source of autonomous long‐duration aerostat, whose power generation predicting accuracy and speed affect the subsequent flight control strategy. Limited by incompleteness cognition of near space, current predicting results cannot meet the requirements of autonomous LDA. In this paper, a novel rapid prediction model of the PV array is proposed. Based on spatial position relation of PV cells, this model can predict the power of single PV cell in any state. The four influence factors including time difference, angle, efficiency loss and temperature are analyzed and optimized comprehensively and innovatively. The new model can be applied in both static state aerostat and dynamic state aerostat. Compared with the traditional model, the efficiency can be improved by 50% and the prediction accuracy can be improved by 10%.
Databáze: Directory of Open Access Journals
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