Artificial Neural Network-Based Constrained Predictive Real-Time Parameter Adaptation Controller for Grid-Tied VSCs

This article proposes a real-time algorithm for identifying the grid parameters, which is concurrently used for online tuning of the predictive controller in each iteration, in a grid-tied active front end (AFE) voltage source converter (VSC) applications. The algorithm is designed by inspiring from the concepts of the extended Kalman filter (EKF) and the model predictive control (MPC). The performance of the algorithm highly depends on the weighting factors of the algorithm. The artificial neural network (ANN)-based algorithm is used to find the optimal set of weighting factors among the ones in a parameter search block. An offline particle swarm optimization (PSO) is run to provide the data source for the parameter search block. The algorithm identifies not only the inductance but also the resistance of the grid. In addition, the hard constraints on the amplitude of the input and output variables are guaranteed. The validation of the proposed approach is performed experimentally and compared with the state-of-the-art conventional methods. The experimental results show that the proposed method could effectively stabilize the system in weak grid conditions and under wide impedance variations. In addition, the accuracy of the proposed impedance identification method is 96%.