ANN based peak power tracking for PV supplied DC motors

This paper presents an application of an Artificial Neural Network (ANN) for the identification of the optimal operating point of a PV supplied separately excited dc motor driving two different load torques. A gradient descent algorithm is used to train the ANN controller for the identification of the maximum power point of the Solar Cell Array (SCA) and gross mechanical energy operation of the combined system. The algorithm is developed based on matching of the SCA to the motor load through a buck-boost power converter so that the combined system can operate at the optimum point. The input parameter to the neural network is solar insolation and the output parameter is the converter chopping ratio corresponding to the maximum power output of the SCA or gross mechanical energy output of the combined PV system. The converter chopping ratios at different solar insolations are obtained from the ANN controller for two different load torques and are compared with computed values.