Intelligent Control and Multi-Level Inverter Design for Power Management using Deep Learning

For low- to medium-power needs, this multiple converter inverter device was developed. Electricity may be stored in some integrators, which are utilized most often in descending multiple integrators. These reactors provide a lot of advantages, but their longevity is called into doubt by the presence of additional circuits in the design. Integrators with low switching frequencies and high predictability are notoriously difficult to build, according to studies (THD). In this work, we suggest PV systems that link to the grid through several inverters. In this investigation, a convolutional neural network (CNN) model was used to examine the total harmonic distortion (THD) of the current drawn from a three-phase, three-stage inverter circuit. By adjusting the switching frequency of the inverter’s circuits, a range of THD values for the specified rectifier supply were calculated using Newton’s Direct Xrf (FFT) calculation. During the training and testing stages of the network advanced AI approach, this dataset is utilized to determine the THD value of the current. To aid those designing electrical circuits, we give a case study of using a convolutional neural network (CNN) to estimate parametric data from a power electronics circuit simulation using Matlab and parametric design research. Average absolute error (MAE) values for testing and training at the conclusion of the estimate, are 0.1894% and 0.4009%, correspondingly.