Proactive and Reactive Maintenance Strategies for Self-Healing Digital Twin Islanded Microgrids Using Fuzzy Logic Controllers and Machine Learning Techniques

The increasing scarcity of fossil fuels has led to a growing emphasis on renewable energy sources, particularly in rural areas where they are integrated into the existing power grid. However, these integrated renewable energy systems often lack immediate support in the event of failures or disruptions. The concept of a digital twin, which replicates the behavior of physical components, can be leveraged to proactively and reactively address such situations. Monitoring companies can utilize this infrastructure to accurately plan energy generation capacity and identify potential threats in specific areas. This enables advanced grid management and facilitates the integration of microgrids through predictive maintenance and load-side management. In order to promote these benefits, this research focuses on the self-healing of a microgrid, dividing it into proactive and reactive maintenance strategies. Proactive maintenance is achieved through the use of fuzzy controllers, which work to prevent failure situations by controlling the operation of the microgrid. Additionally, a random forest algorithm is employed to classify failure scenarios and optimize the load schedule. By effectively managing the actuation states of the microgrid and offering optimized downtime for terminal loads, this approach aims to prevent failures and ensure efficient operation.