Modeling faulted switched reluctance motors using evolutionary neural networks

The work presented examines the feasibility of using artificial neural networks (ANNs) and evolutionary algorithms (EAs) to model fault free and faulted switched reluctance motor (SRM) drive systems. SRMs are capable of functioning despite the presence of faults. Faults impart transient changes to machine inductances in a manner that is difficult to model analytically. After this transient period, SRMs are capable of functioning at a reduced level of performance. ANNs are applied for their well known interpolation capabilities for highly nonlinear systems. EAs are employed for their ability to search a complex structural and parametric space as necessary to find good ANN solutions. In this paper, the ANN structure and training regimen are described for application to an example SRM drive system under normal and abnormal operating conditions