Rapid Power System Transient Stability Limit Search Using Signal Energy and Neural Networks

In very recent work, researchers showed that the signal energy of the system response is indeed such a useful physical quantity, and that there is a sure relationship between this physical quantity and the system transfer limits. However, this relationship is still unable to be modeled. Here, artificial neural networks are the best to be used in such cases where input-output mappings are unknown or difficult to be modeled. The basic strategy for this paper is to use artificial neural networks for mapping power system signal energies into the power system transfer limits; i.e. to extract the relationship between power system transfer limits and the system signal energy. Primary results show that the neural network, after being trained, is able to accurately predict the power system transfer limit by looking to the signal energy measured at different buses in the system