Harmonic Elimination Control of an Inverter Based on an Artificial Neural Network Strategy

AbstractA neural implementation of a harmonic elimination strategy for the control a uniform step asymmetrical 11-level inverter is proposed and described in this paper. A Multi-Layer Perceptrons (MLP) neural network is used to approximate the mapping between the modulation rate and the required switching angles. After learning, the neural network generates the appropriate switching angles for the inverter. This leads to a low-computational-cost neural controller which is therefore well suited for real-time applications. This neural approach is compared to the well-known sinusoidal Pulse-Width Modulation (PWM). Simulation results demonstrate the technical advantages of the neural implementation of the harmonic elimination strategy over the conventional method for the control of an uniform step asymmetrical 11-level inverter. The approach is used to supply an asynchronous machine and results show that the neural method ensures a highest quality torque by efficiently canceling the harmonics generated by the inverter.