Heliostat drift prediction model to improve heliostat position control in solar fields

Abstract

One objective for the 3rd Generation of Concentrating Solar Power technologies is to improve the efficiency of Central Tower, which is among the most promising solar concentration technologies. Central Tower Power Generation Plants are made up of hundreds of heliostats that redirect solar radiation to a target. The precision of the solar tracking is affected by a series of cumulative errors generated by the structure, the installation, and the sun tracking system. The present work describes a practical methodology for generating a prediction model to calculate the drift errors of a heliostat. The methodology for predicting annual drift behavior focuses on heliostat deterministic misalignment and installation errors due to the most significant sources of drift error. These sources are the angular reference offset, pedestal tilt, and canting errors. The drift prediction is based on the assumption that the drift curve of a heliostat is the linear sum of the drift curves from these individual errors. This model can be used to predict the drift of a heliostat for any value of solar declination based on only one drift curve performed on any day of the year. The proposed model can provide means for drift prediction affecting each heliostat that can be used to compensate the tracking error. This understanding allows for correct tracking and increases the solar field’s optical efficiency, thereby enhancing the efficiency of a solar tower plant in real-world applications.