Deriving a transient stability index by neural networks for power-system security assessment

This paper proposes an approach for establishing a transient stability clas sifier and derives a continuous transient stability index, using a three-la yer feed-forward artificial neural network (ANN), for on-line security asse ssment in large power systems. With the derived stability index, a never cl assification scheme creating an 'indeterminate' class, is introduced to min imize misclassifications and to improve the reliability of the classificati on results. Several post-fault abstract attributes about the system generat ors' acceleration rates and kinetic energies provide the basis for the stab ility classification. In order to derive the transient stability index, a s emi-supervised backpropagation (BP) learning algorithm, making use of a spe cially defined error function, is developed. The proposed approach can not only distinguish whether a power system is stable or unstable, on the basis of the specific post-fault attributes, but can also provide a relative sta bility quantifier. Furthermore, as the number of the selected abstract attr ibutes is independent of the system size, the methodology of the proposed a pproach can realistically be applied to large power systems. The 10-unit 39 -bus New England power system is employed to demonstrate the proposed appro ach. The numerical results show that the ANN-based classifier can assess th e transient stability reasonably well. (C) 1998 Elsevier Science Ltd. All r ights reserved.