Application of artificial neural networks in power system security and vulnerability assessment

In a companion paper by A.A. Fouad et al. the concept of system vulnerability is introduced as a new framework for power system dynamic security assessment. Using the transient energy function (TEF) method of transient stability analysis, the energy margin ΔV is used as an indicator of the level of security, and its sensitivity to a changing system parameter p (δΔV/δp) as an indicator of its trend with changing system conditions. These two indicators are combined to determine the degree of system vulnerability to contingent disturbances in a stability-limited power system. Thresholds for acceptable levels of the security indicator and its trend are related to the stability limits of a critical system parameter (plant generation limits). Operating practices and policies are used to determine these thresholds. In this paper the artificial neural networks (ANNs) technique is applied to the concept of system vulnerability within the recently developed framework, for fast pattern recognition and classification of system dynamic security status. A suitable topology for the neural network is developed, and the appropriate training method and input and output signals are selected. The procedure developed is successfully applied to the IEEE 50-generator test system. Data previously obtained by heuristic techniques are used for training the ANN