Optimized Control of a Dual-Axis PV Solar Tracker using a Radiometric Cube and an Artificial Neural Network

This work presents a new control algorithm for a dual-axis solar tracker based on the irradiance measured on four faces of a radiometric cube associated to an artificial neural network (ANN). The control algorithm directly with the radiometric cube without ANN showed a 40.5% gain in energy output compared with a chronometric Sun tracking algorithm on a cloudy day. However, the control algorithm in this configuration showed a loss of 1 % during a period with a clear sky and with highly variable weather conditions. Including an ANN model in the control algorithm with the radiometric cube could reduce the errors that caused losses during periods of clear sky and periods with highly variable weather conditions. The ANN model is used to estimate the PV output power for ten chosen directions of the solar tracker. The ten chosen directions are defined from the irradiance on four faces of a radiometric cube. The PV power output in these ten directions is estimated to determine the direction that maximizes the PV power output for the next movement of the solar tracker. An evaluation of different training datasets was carried out to determine the appropriate amount of data that is relevant for an ANN model with high performance. The evaluation showed that training the ANN on a one-year dataset resulted in good performances that are almost similar to the performances of an ANN trained on a 2-year dataset. The new proposed control algorithm is compared experimentally with a standard chronometric Sun tracking algorithm. The analysis of the comparison showed that the proposed algorithm provides a gain of 1.4% in energy produced during a clear sky period with occasional clouds.