An Artificial Neural Network Approach for Solving the Harmonic Distortions Elimination in Multilevel Converters

Abstract

In this paper, a neural approach is applied to determine the switching angles for a uniform step asymmetrical multilevel inverter by eliminating specified higher-order harmonics while maintaining the required fundamental voltage. A Multi-Layer Perceptron (MLP) neural network is used to approximate the function between the modulation rate and the required switching angles. After learning, the artificial neural network is able to determine 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 technique can be applied to multilevel inverters with any number of levels. As an example, a nine-level inverter and an eleven-level inverter are considered and the optimum switching angles are calculated on-line. The neural approach is compared to the well-known sinusoidal Pulse-Width Modulation (PWM) strategy. Simulation results demonstrate the better performances and technical advantages of the neural controller in feeding an asynchronous machine. Indeed, the harmonic distortions are efficiently cancelled providing thus an optimized control signal for the asynchronous machine. Moreover, the technique presented here substantially reduces the torque undulations.