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Design of a binary neural network for security classification in power system operation
Conference proceeding

Design of a binary neural network for security classification in power system operation

H.H Yan, J.-C Chow, M Kam, C.R Sepich and R Fischl
1991 IEEE International Symposium on Circuits and Systems (ISCAS), pp 1121-1124 vol.2
1991

Abstract

Classification algorithms Intelligent networks Load flow Neural networks Pattern recognition Power system analysis computing Power system measurements Power system modeling Power system security Voltage
The authors present a method for designing a neural network (NN) for potential application in real-time system security analysis. Specifically, the authors 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.< >

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