Improvement in power system transient stability by using STATCOM and neural networks

Abstract

Transient stability problem is very important for the secure operation of today’s heavily loaded power systems. In this paper, an auxiliary (supplementary) controller is employed for static synchronous compensator (STATCOM) to improve the transient stability limit of multimachine power systems. In the proposed controller, the angle and speed of a severely disturbed machine are used to change the shunt susceptance of STATCOM in the transient period. The developed control method is successfully applied to the New England 10-machine, 39-bus test system. The simulation results obtained, however, show that to have a desired critical clearing time for a particular fault scenario, the gain of the STATCOM auxiliary controller needs to be adjusted according to the system operating point. On the other hand, calculating the required gain of the STATCOM controller via a trial-and-error approach and performing several time-domain simulations is very time-consuming. To speed up the method of calculating that gain in online applications, a multilayered perceptron (MLP) neural network (NN)-based approach is proposed in this paper. The pre-fault system operating conditions are chosen as the inputs of the MLP NN. In addition, an intuitive-based method is represented to reduce the number of neural network inputs. The simulation results obtained by the proposed MLP NN-based method are also presented and discussed in detail.