DocumentCode :
3147635
Title :
Hybrid expert system neural network hierarchical architecture for classifying power system contingencies
Author :
Yan, H.H. ; Chow, J.-C. ; Kam, M. ; Fischl, R. ; Sepich, C.R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA
fYear :
1991
fDate :
23-26 Jul 1991
Firstpage :
76
Lastpage :
82
Abstract :
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
Keywords :
expert systems; neural nets; power system analysis computing; expert system; heuristic rules; hierarchical architecture; neural network; performance indicators; power system contingencies; 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;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks to Power Systems, 1991., Proceedings of the First International Forum on Applications of
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0065-3
Type :
conf
DOI :
10.1109/ANN.1991.213501
Filename :
213501
Link To Document :
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