A Review on Open Circuit Failure Analysis of Power Electronic Converters Using AI Techniques

The steady functioning of power electronic-based systems is linked to fault localization and identification for converters, which is gaining a lot of attention. Artificial intelligence (AI), a representation of sophisticated algorithms is offering new solutions. This article examines how artificial intelligence (AI) can be used to diagnose open-circuit defects in power electronic converters, which are essential parts of contemporary power systems. From an algorithmic point of view, the fault diagnosis techniques are methodically classified and summarized encompassing a variety of approaches such as multimodal fusion, hybrid models, neural networks (such as convolutional and recurrent neural networks), and traditional machine learning (such as support vector machines and extreme learning machines). Their effectiveness is assessed in terms of real-time capabilities, computational efficiency, and noise robustness. Furthermore, future possibilities and suggested paths for AI-driven fault diagnosis are presented, promoting advancements to improve power converter systems' dependability and affordability. To support the creation of sophisticated diagnostic.