An artificial-neural-network method for the identification of saturated turbogenerator parameters based on a coupled finite-element/state-space computational algorithm
- Clarkson Univ., Potsdam, NY (United States). Electrical and Computer Engineering Dept.
- Marquette Univ., Milwaukee, WI (United States). Electrical and Computer Engineering Dept.
An artificial neural network (ANN) is used in the identification of saturated synchronous machine parameters under diverse operating conditions. The training data base for the ANN is generated by a time-stepping coupled finite-element/state-space (CFE-SS) modeling technique which is used in the computation of the saturated parameters of a 20-kV, 733-MVA, 0.85 pf (lagging) turbogenerator at discrete load points in the P-Q capability plane for three different levels of terminal voltage. These computed parameters constitute a learning data base for a multilayer ANN structure which is successfully trained using the back-propagation algorithm. Results indicate that the trained ANN can identify saturated machine reactances for arbitrary load points in the P-Q plane with an error less than 2% of those values obtained directly from the CFE-SS algorithm. Thus, significant savings in computational time are obtained in such parameter computation tasks.
- OSTI ID:
- 237886
- Report Number(s):
- CONF-950103-; ISSN 0885-8969; TRN: IM9625%%211
- Journal Information:
- IEEE Transactions on Energy Conversion, Vol. 10, Issue 4; Conference: Winter meeting of the IEEE Power Engineering Society, New York, NY (United States), 29 Jan - 2 Feb 1995; Other Information: PBD: Dec 1995
- Country of Publication:
- United States
- Language:
- English
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