Acceleration of Waveform Control for Measurement of Magnetic Hysteresis Based on Single Sheet Tester Using Neural Network

In recent years, the high-quality design of various motors to realize high efficiency is enthusiastically demanded owing to the promotion of electric vehicles. Therefore, it is essential to accurately evaluate of the motor design using the finite element analysis. Furthermore, the accurate measurement data of magnetic property obtained employing the single sheet tester (SST) is also required. However, when the measurement of magnetic hysteresis under the condition that high magnetic flux density on the low-frequency region is operated, the number of feedback iterations for the flux waveform control tends to increase owing to the excess distortion of the excitation voltage waveform. Consequently, since the elapsed time for the measurement also tends to increase, the development of a method to reduce the elapsed time is substantially required. In this paper, to reduce the elapsed time for the measurement of the magnetic hysteresis, the convergence characteristics of the flux waveform control are accelerated by the estimation of the initial waveform of the excitation voltage waveform using a neural network (NN).