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Border identification for power system security assessment using neural network inversion: an overview

Knowledge of the dynamic security border can provide an operator with valuable information on how to safely steer the power system away from vulnerable operating regions. The large set of non-linear differential equations that describe modern large-scale power systems makes it difficult to determine the security border either analytically or numerically in real time. As an alternative, neural networks trained off line on emulator data can provide a commensurate representation of the system transfer function, while significantly decreasing evaluation time. Using neural network inversion, sets of input points corresponding to a fixed output can be evaluated quickly. Different inversion procedures and their properties are reviewed here. We also review various metrics used for determining whether sufficient coverage of the border is achieved. Finally, we illustrate the use of border identification for preventive control when feature selection is initially performed to reduce the dimensionality of the input space.