The estimation of iron losses in a non-oriented electrical steel sheet based on the artificial neural network and the genetic algorithm approaches

Cold rolled non-oriented (CRNO) electrical steel sheets are soft ferromagnetic materials which are commonly used for electromagnetic core design for AC rotating electrical machines. When these materials are exposed to time-varying magnetic fields, the iron losses occur. These losses represent the power dissipated in the ferromagnetic material and they are dependent upon the frequency and magnetic flux density level of the applied time-varying magnetic field. In order to achieve high-efficiency electrical machines, especially at high operating frequencies and magnetic flux density levels, iron losses should be kept as low as possible. This imposes the need for more accurate iron losses models, but also for fast and reliable estimation techniques. This paper considers the applications of an artificial neural network (ANN) and a genetic algorithm (GA), based on the classical iron losses separation formulation for a fast estimation of the specific iron losses in CRNO electrical steel sheet grade M530-50A over a wide frequency and magnetic flux density range. Iron losses measurement data, provided by the manufacturer, are used to calibrate the iron losses models. The approaches were verified using the manufacturer's measurement data. Acceptable accuracy was obtained.