Journal
IEEE TRANSACTIONS ON ENERGY CONVERSION
Volume 18, Issue 2, Pages 284-290Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEC.2003.811738
Keywords
modeling; neural network; parameter identification; switched reluctance motor
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Phase windings of switched reluctance machines are modeled by a nonlinear inductance and a resistance that can, be estimated from standstill test data. During online operation, the model structures and parameters of SRMs may differ from the standstill ones because of saturation and losses, especially at high current. To model this effect, a damper winding is added into the model structure. This paper proposes an application of artificial neural network to identify the nonlinear model of SRMs from operating data. A two-layer recurrent neural network has been adopted here to estimate the damper currents from phase voltage, phase current, rotor position, and rotor speed. Then, the damper parameters can be identified using maximum likelihood estimation techniques. Finally, the new model and parameters are validated from operating data.
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