Transient stability evaluation of electrical power system using generalized regression neural networks

Abstract

Transient stability evaluation (TSE) is part of dynamic security assessment of power systems, which involves the evaluation of the system's ability to remain in equilibrium under credible contingencies. Neural networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of power systems. This paper proposes a generalized regression neural networks (GRNN) based classification for transient stability evaluation in power systems. In the proposed method, learning data sets have been generated using time domain simulation (TDS). The GRNN input nodes representing the voltage magnitude for all buses, real and reactive powers on transmission lines, the output node representing the transient stability index. The proposed GRNN was implemented and tested on IEEE 9-bus and 39-bus test systems. NN results show that the stability condition of the power system can be predicted with high accuracy and less misclassification rate.