Artificial neural network pattern classification of transient stability and loss of excitation for synchronous generators
✍ Scribed by A.M. Sharaf; T.T. Lie
- Publisher
- Elsevier Science
- Year
- 1994
- Tongue
- English
- Weight
- 593 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0378-7796
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✦ Synopsis
A novel artificial intelligence based neural network (ANN) global online fault detection, pattern classification, and relaying detection scheme for synchronous generators in interconnected electric utility networks is presented. The input discriminant vector comprises the fast Fourier transform (FFT) dominant frequency spectra of eighteen input variables forming the discriminant diagnostic hyperplane. The online ANN based relaying scheme classifies fault existence, fault type as either transient stability or loss of excitation, the allowable critical clearing time, and loss of excitation type as either open-circuit or short-circuit field conditions. The proposed FFT dominant-frequency based hyperplane diagnostic technique can be easily extended to multimachine electric interconnected AC systems.