An Adaptive Strategy to Compensate Nonlinear Effects of Voltage Source Inverters Based on Artificial Neural Networks

Abstract

In motor drives, distortions of phase voltages and currents are often caused by nonlinear effects of inverters such as dead time, turn-on delay, turn-off delay and voltage drop of power devices. To eliminate these distortions, the dead-time compensation voltage is usually investigated. Furthermore, the relationship between the dead-time compensation voltage and phase currents is nonlinear, which is related to not only the parameters mentioned above, but also the snubber and parasitic capacitance of inverters. A nonlinear function is constructed to model the nonlinear relationship in this paper. To identify the nonlinear function, a method based on artificial neural networks is proposed without inverter parameters. According to the criterion that the trajectory of voltage vector in α - β coordinate system is a circle, an adaptive law is constructed to modify the parameters of the nonlinear function. Therefore, the nonlinear dead-time compensation voltage model is obtained accurately, where the distortions of voltages and currents are reduced without any additional hardware. Applying this method to the current predictive control, the bandwidth of a current loop is increased by 500Hz. Effectiveness of the method is verified by experiments.