Design of adaptive load shedding by artificial neural networks

Abstract

The design of an adaptive load-shedding strategy by executing an artificial neural network (ANN) and transient stability analysis for an electric utility system is presented. To prepare the training data set for an ANN, the transient stability analysis of an actual power system has been performed to solve for minimum load shedding with various operation scenarios without causing the tripping problem of generators. The Levenberg–Marquardt algorithm has been adopted and incorporated into the back-propagation learning algorithm for training feedforward neural networks. By selecting the total power generation, total load demand and frequency decay rate as the input neurons of the ANN, the minimum amount of load shedding is determined to maintain the stability of power systems. To demonstrate the effectiveness of the proposed ANN minimum load-shedding scheme, a utility power system has been selected for computer simulation and the amount of load shedding is verified by stability analysis.