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Generalization of transient stability solution using neural network theory
Author(s) -
Ikeo Katsumi,
Iwamoto Shinichi
Publication year - 1992
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391120308
Subject(s) - transient (computer programming) , generalization , artificial neural network , control theory (sociology) , stability (learning theory) , backpropagation , computer science , generator (circuit theory) , activation function , energy (signal processing) , transient response , function (biology) , mathematics , artificial intelligence , engineering , machine learning , control (management) , physics , mathematical analysis , power (physics) , statistics , quantum mechanics , operating system , evolutionary biology , electrical engineering , biology
This paper presents a generalized online transient stability solution technique using the backpropagation method of the neural network theory. The proposed solution technique can be used for general generator models, including controllers such as AVRs and governors, that have been difficult for the energy function method to handle. The proposed method also considers alterations of network configurations and changes of the number of operating generators. Further, the relationship between input data and effects on the results of the neural network is considered.