ABSTRACT The sharp increase in global energy demand, the finite nature of current resources, the necessity for clean and sustainable sources, and the need for uninterrupted and low‐cost energy have led to the search for new solutions. The current energy production methods must be sufficient, significantly increasing the interest and necessity for microgrids. Microgrids stand out with their potential to bring flexibility, reliability, and sustainability to energy systems. However, microgrids' complex structure and dynamic nature have various challenges in fault detection. Types and impacts of microgrid faults vary depending on the microgrid topology, operating mode, and distributed generation sources. Therefore, these faults' prediction, management, and solution should be comprehensively addressed. This study provides a detailed analysis of the current methods and technologies used for fault diagnosis in microgrids. Fault detection methods in the state of the art are compared, including signal processing–based approaches, artificial intelligence–based approaches, and traditional techniques. Additionally, fault management strategies and their effectiveness are evaluated in microgrids. The study also examines the benefits provided in areas such as protection strategies and predictive maintenance recommendations beyond identifying fault presence. In conclusion, future research areas and potential improvement opportunities are discussed to enhance the safe and continuous operation of microgrids.
Hasır et al. (Thu,) studied this question.