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Hybrid expert system neural network hierarchical architecture for classifying power system contingencies
Conference proceeding

Hybrid expert system neural network hierarchical architecture for classifying power system contingencies

H.H Yan, J.-C Chow, M Kam, R Fischl and C.R Sepich
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems
1991
DOI: https://doi.org/10.1109/ANN.1991.213501

Abstract

Application specific processors Computer architecture Expert systems Hybrid power systems Neural networks Performance analysis Power system analysis computing Power system reliability Power system security Voltage
The authors present a hierarchical architecture which couples an expert system (ES) with multiple neural networks (NNs) for classifying power system contingencies. The ES performs the 'coarse' screening to decide if a contingency is potentially harmful and then determines its type of security limit violations. It uses a set of heuristic rules and a set of performance indicators to filter out the secure contingencies and direct the potentially harmful ones for further analysis in the appropriate NN. The NN's take the coarse classification outcome from the ES and perform a 'finer' screening by classifying the contingencies according to the severity of limit violations.< >

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