A non-intrusive optical approach to characterize heliostats in utility-scale power tower plants: Flight path generation/optimization of unmanned aerial systems. (1st September 2021)

• Record Type: Journal Article
• Authors: Farrell, Tucker; Guye, Kidus; Mitchell, Rebecca; Zhu, Guangdong
• Is Part Of: Solar energy. Volume 225(2021)
• Page Start: 784
• Page End: 801
• Publication Date: 2021-09-01
• Subjects: Concentrating solar power -- Unmanned aerial system -- Heliostat optical errors; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2021.07.070
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Unmanned Aerial Systems provide an efficient means to collect CSP heliostat image data in-situ without interrupting plant operation. The UAS can be programmed to collect data autonomously, satisfying constraints and user parameters to achieve images used for heliostat error characterization. The images can be used to characterize 3 main sources of heliostat errors: slope, canting, and tracking. The UAS is a novel approach to rapidly acquiring a large volume of this image data which can be used to benefit CSP power generation efficiency. Abstract: A newly developed in situ non-intrusive optical (NIO) approach has been developed to survey various types of heliostat optical errors for a concentrating solar power (CSP) tower plant. To measure mirror surface slope error, facet canting error, and heliostat tracking error at a sub-milliradian accuracy, NIO requires several reflection images scanned over each individual heliostat. For a utility-scale plant that typically includes more than 10, 000 heliostats, an unmanned aerial system (UAS) is crucial for efficient implementation of the NIO method. In this paper, we develop a flight path generation/optimization algorithm to plan more efficient UAS paths to collect NIO data over a utility-scale heliostat field. The algorithm considers NIO data requirements, all potential constraints, optimization within each subfield, and operational flexibility. Case studies are presented to illustrate the feasibility and robustness of the developed flight path algorithm. The path planning algorithm may also find applications elsewhere, such as drone-driven imaging under extreme conditions.