A New Tool for Large Scale Power System Transient Security Assessment

In this paper, the nonlinear mapping relation between the transient energy margin and the generator power at different fault clearing time and load levels was established by using the self-organizing feature map. Lyapunov’s direct method was used as a fast method to obtain the training set of the neural network. The transient security power limits of the generator at different fault clearing time and load levels were estimated very fast by the self-organizing mapping. A comparative study with the analytical sensitivity approach shows that the proposed method avoids the necessity of finding the analytical sensitivity of the transient energy margin to parameter changes, and can fast estimate transient security power limits at different fault clearing time and load levels of the system. Preliminary results carried out on a 7 machine-CIGRE test system are reported.