A Survey on Deep Neural Network (DNN) Based Dynamic Modelling Methods for Ac Power Electronic Systems

Abstract

This research work contains the assessment of a deep neural network (DNN) based dynamic modeling scheme for AC power electronic systems. The study is based on the premise of utilization of deep learning algorithms to derive models that are accurate and dynamic for capturing the aspects that are complex in AC power electronics systems. Nonlinear relationships and variability in operating conditions make it challenging to apply traditional modeling; thus, a possibility to apply DNNs is considered due to their data-driven learning compared to conventional feature-oriented techniques. It is a process of training and testing of the developed DNN models on the data sets, developed from the AC power electronic systems, under various operational conditions. Satisfaction is measured based on performance indicators that individuals employ, for instance, accuracy, resilience to different loads, and computational speed that justifies the proposed approach. As per the obtained results, the proposed DNN-based models for the four classes have better prediction accuracies compared to conventional techniques for real-time continuous control and fault diagnosis in AC power electronic systems. This work fits within the advancements of the field by offering a detailed evaluation of DNNs as a valid means for dynamic modeling within the scope of AC power electronics in order to critic and improve the performance and dependability of more practicable applications.