Artificial neural network model for voltage security based contingency ranking
Applied Soft Computing
Coherency-based fast voltage contingency ranking employing counterpropagation neural network
Engineering Applications of Artificial Intelligence
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
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Maintaining power system security in the deregulated and unbundled electricity market is a challenging task for power system engineers. The idea is to short-list critical contingencies from a large list of contingencies and to rank the contingencies expected to drive the system towards instability. Timely corrective measures can then be planned to save the system from collapse and blackout. This paper presents a simple multi-output fuzzy-neural network for contingency ranking in a power system. A fuzzy composite performance index (FCPI), formulated by combining (i) voltage violations, (ii) line flow violations and (iii) voltage stability margin is being proposed in this paper for composite ranking of contingencies. The proposed approach is very effective in handling contingencies lying on the boundary between two severity classes. Feature selection using fuzzy curves has been employed to reduce the dimension of the network. The performance of the proposed method has been tested on a 69-bus practical Indian power system.