Neural network predictive control of UPFC for improving transient stability performance of power system

This paper presents a neural network predictive controller for the UPFC to improve the transient stability performance of the power system. A neural network model for the power system is trained using the backpropagation learning method employing the Levenberg–Marquardt algorithm for faster convergence. This neural identifier is then utilized during predictive control of the UPFC. The damped Gauss–Newton method employing ‘backtracking’ as the line search method for step selection is used by the predictive controller to predict the future control inputs. The 4- machine 2-area power system which is a benchmark power system is used to demonstrate the performance of the proposed controller. The system under consideration is simulated for different transients over a range of operating conditions using Matlab/Simulink. The proposed neural network predictive controller exhibits superior damping performance in comparison to the conventional PI controller. The simulation results also establish convergence of the minimization algorithm to an acceptable solution within single iteration.

► Transient stability of the UPFC equipped 2-area, 4- machine system is investigated. ► Neural Network Predictive Control (NNPC) for UPFC is examined. ► NNPC for UPFC enhances damping in the system subjected to transients. ► NNPC compares favorably with PI control over a wide range of operating points.