A rapid prediction model of photovoltaic power generation for autonomous long‐duration aerostat. Issue 6 (20th February 2023)

• Record Type: Journal Article
• Authors: Liu, Qianshi; Xu, Guoning; Li, Zhaojie; Gao, Yang; Yang, YanChu; Li, Yongxiang; Du, Hao
• Is Part Of: IET renewable power generation. Volume 17:Issue 6(2023)
• Page Start: 1597
• Page End: 1608
• Publication Date: 2023-02-20
• Subjects: aerospace vehicles -- correction model -- PV power -- PV prediction model; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/rpg2.12697
• Languages: English
• ISSNs: 1752-1416

Abstract

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%. Abstract : This paper proposes a distributed optimal dispatching method considering transient stability constraint in microgrid. It offers a compromise between the security and economy of microgrid.