Development of a neural network based saturation model for synchronous generator analysis
- Ohio State Univ., Columbus, OH (United States)
- Arizona Public Service Co., Phoenix, AZ (United States)
This paper presents a new approach to model the synchronous generator saturation based on a feed-forward artificial neural network (ANN) model. The machine loading conditions, excitation levels and rotor positions are all included in the modeling process. The nonlinear saturation characteristics of a three-phase salient-pole synchronous machine rated at 5 kVA and 240 V is studied using the ANN model. An appropriate selection of input/output pattern for the ANN model training based on error back-propagation scheme is developed using the on-line small-disturbance responses and the well-known maximum-likelihood estimation algorithm. The developed ANN model is implemented in the generator dynamic transient stability study requiring only small computational alteration in saturation model representation.
- OSTI ID:
- 237885
- Report Number(s):
- CONF-950103--
- Journal Information:
- IEEE Transactions on Energy Conversion, Journal Name: IEEE Transactions on Energy Conversion Journal Issue: 4 Vol. 10; ISSN 0885-8969; ISSN ITCNE4
- Country of Publication:
- United States
- Language:
- English
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