An Improved Artificial Neural Network-Based Approach for Total Harmonic Distortion Reduction in Cascaded H-Bridge Multilevel Inverters

The concept of smart grids has enabled the addition of renewable energy resources in the utility grids. This integration is possible after energy conversion and energy conversion causes the degradation in the quality of electric power. The power quality can be enhanced by reducing the total harmonic distortion (THD) in energy conversion. In this paper, we have presented an improved artificial neural network (ANN) based approach that can be useful to reduce the THD levels in a cascaded H-bridge (CHB) multilevel inverter (MLI). The proposed ANN architecture consists of only one hidden layer with ten neurons and can generate accurate results in both overfitting and underfitting conditions. Due to a smaller number of neurons, the proposed architecture is less complex and can produce results relatively faster as compared to other ANN architectures available in the literature. The proposed ANN architecture is tested on an asymmetrical CHB MLI, and the results are compared with the recent state-of-the-art (SOTA) techniques. The CHB MLI under test had three DC voltage sources with 1:2:4 and inductive loads. The simulations are performed in MATLAB and Simulink environment and the switching angles are optimized by using the ANN architecture. The results have shown a 71.95% improvement in current THDs and a 13.91% improvement in voltage THDs as compared to the SOTA technique. The proposed configuration is 89.6% efficient on inductive load and uses a smaller number of switches. Finally, an experimental setup is created, and the simulation results are validated through various experiments.