Abstract
This paper presents a method for automatic contingency selection and static security evaluation of electrical power systems. The method employs multi-layer Perceptron neural networks whose inputs are power flows and injections, while the outputs identify potentially harmful contingencies. The performance of the method is evaluated for different operating conditions using the IEEE 24 bus test system. It is shown that the neural network classifiers perform very well the contingency selection task and enables a previous classification of system operating state with respect to static security..
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