Neural Network Based Modeling and Parameter Identification of Switched Reluctance Motors

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 SRM’s 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 SRM’s from operating data. A 2-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. Index Terms — Neural Network, Modeling, Parameter Identification, Switched Reluctance Motor