Uncertainty Analysis of Heliostat Alignment at the Sandia Solar Field

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• 作者：Rachel Hogan^a ; John Pye^a ; Clifford Ho^b ; Edward Smith^b
• 关键词：solar thermal ; heliostat ; training data ; numerical modelling ; synthetic data
• 刊名：Energy Procedia
• 出版年：2014
• 年：2014
• 卷：49
• 期：Complete
• 页码：2100-2108
• 全文大小：675 K

文摘

Low-cost heliostats with open-loop tracking systems require careful calibration in order to track the sun accurately. This calibration can be done by mechanical adjustment, which increases the cost of both the components and commissioning, or it can be done automatically, using software, by ‘learning’ the various forms of misalignment present in a particular heliostat, and adjusting the pointing directions in order to cancel out the effect of those misalignments. A large set of training data will allow these corrections to be determined to quite high accuracy, and several of low-cost heliostat concepts have already been developed which make use of some form of this principle to reduce overall CSP system cost, though the methods used have not been thoroughly described in open literature.

The current study builds upon earlier work by Baheti and Scott (1980), Khalsa et al (2011) and Pye and Zhang (2012), to analyze the process of automated misalignment correction with the introduction of an uncertainty analysis applied to an experimental training data set. The accuracy of correction process from this experimental data is quantified, allowing a criterion to be applied to determine whether or not sufficient training has been completed for each heliostat to mean overall field accuracy requirements. To investigate the potential improvements from extended training, a synthetic data set is generated, and used to investigate preferred times of year and times of data for training specific heliostats in the field. Summer data is shown to be best, but the additional of some winter data is helpful. Time-of-day is also important, especially for the sides of the heliostat field; middle-of- the-day training and spring or autumn training are seen to be less effective. A training programme for the entire heliostat field is presented and discussed: each heliostat is trained daily in summer for two minutes, and daily in winter for one minute in the morning and evening, resulting in 95% certainty that all heliostats will have their focal spot within 1.5 m of the target for the entire year, by an entirely automated process.