High-Speed Fault Detection and Location Approach for Multilevel Inverters Using Deep Learning and Reliability Evaluation

Abstract

Multilevel power inverters have a complex semiconductor structure that elevates the risk of switch faults. Furthermore, voltage drops across floating capacitors, which are integral components of multilevel power converter structures, can disrupt accurate system status assessment and lead to incorrect or delayed fault detection. This article proposes a novel approach for short-circuit fault detection and location in multilevel power converters using artificial intelligence with a focus on reliability prioritization. Five reference voltage prediction methods were analyzed including a switching algorithm and four deep learning-based techniques i.e., convolutional neural networks, gated recurrent units, long short-term memory networks, and a hybrid model combining convolutional neural networks with long short-term memory networks. Fault location was performed through a reliability-based strategy prioritizing components with higher failure probabilities, significantly improving the fault identification speed. Our method reduced the duration of fault detection compared to similar methods and included a novel fault location method based on prioritizing fault detection according to the lifetime of fundamental components. We predicted and verified online voltage references using four different deep learning methods and compare the outcomes in an experimental setup. Simulation and experimental results demonstrated the effectiveness and practicability of the proposed method in detecting and locating faults in various types of multilevel power inverters.