Fault Diagnosis Based Approach to Protecting DC Microgrid Using Machine Learning Technique

The electronic equipment used in DC microgrids is in essential need of more secure protection against short circuit faults. Due to the high current at the time of fault occurrence, the whole system might be de-energized which would have a severely negative impact on the entire system. An effective method to detect, isolate, and protect the DC microgrid system against the effects of short circuit faults is extremely important. This research proposes a highly effective new method to protect the DC microgrid system using an Artificial Neural Network (ANN) that will detect and isolate the fault before it affects other parts of the system. It would, therefore, be more dependable for DC microgrid protection. This protection network is distributed all along the DC microgrid system protecting the entire microgrid network and is connected to the other protective devices in the system.