Estimation of the Solar Power Tower Heliostat Position using Neural Network

Abstract

The alignment and control of heliostats have been one of most issues in solar tower power. In this paper, we review our work to estimate the alignment of solar tower heliostat field based on the combination of two control systems (open loop and closed loop system) using a neural network approach. Several factors influence the path of each heliostat such as the azimuth, the elevation time, the date, the position of the sun, the plant location, the height of the tower and the heliostat size and slope. Firstly, we have modeled the heliostat position using astronomical formulas. Secondly, the neural network model is trained using data from January of three heliostats for both paths. Then, it was tested with another heliostat position for the month of March. The accuracy of the model was evaluated using the mean absolute error (MAE) and absolute percentage error (MAPE). The simulation results show the accuracy of the proposed for estimating the heliostat position without detecting the position of the sun. Keywords Solar tower power, heliostat field, open loop and closed loop system, neural networks.