Design of a Binary Neural Network for Security Classification in Power System Operation

This paper presents a method for designing a Neural Network (") for potential application in real-time system security analysis. Specifically, we formulate the contingency classification problem as a pattern recognition problem and then design a NN to classify the system states (i.e., normal, alert and emergency). A two-layered NN with a fully-connected asynchronous binary model for each layer is developed. An optimization technique, which calculates the weights and thresholds of the NN, is used to maximize the probability of classifying the correct state. This procedure is illustrated through a 17-bus example system for which the post-contingency voltage drop limits are considered.