A Lightweight and Efficient Deep Learning Model for Detection of Sector and Region in Three-Level Inverters

In three-level inverters, high accuracy and low latency sector and region detection are of great importance for control and monitoring processes. This study aims to overcome the limitations of traditional methods and develop a model that can work in real time in industrial applications. In this study, various deep learning (DL) architectures are systematically evaluated, and a comprehensive performance comparison is performed to automate sector and region detection for inverter systems. The proposed approach aims to detect sectors (6 classes) and regions (3 classes) with high accuracy using a Deep Neural Network (DNN), 1D Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) based DL architectures. The performance of the considered DL approaches was systematically evaluated with cross-validation, confusion matrices, and statistical tests. The proposed GRU-based model offers both computational efficiency and high classification performance with a low number of parameters compared to other models. The proposed model achieved 99.27% and 97.62% accuracy in sector and region detection, respectively, and provided a more optimized solution compared to many heavily structured state-of-the-art DL models. The results show that the GRU model exhibits statistically significant superior performance and support that it has the potential to be easily integrated into hardware-based systems due to its low computational complexity. The comprehensive results show that DL-based approaches can be effectively used in sector and region detection in inverter systems, and especially the GRU architecture is a promising method.