Prognostic Health Monitoring of DC Microgrid with Fault Detection and Localization using Machine Learning Techniques

DC microgrids incorporate several converters for distributed energy resources connected to different passive and active loads. The complex interactions between the converters and components and their potential failures can significantly affect the grids' resilience and health; hence, they must be continually assessed and monitored. This paper presents a machine learning-assisted prognostic health monitoring (PHM) and diagnosis approach, enabling progressive interactions between the converters at multiple nodes to dynamically examine the grid's (or micro-grid's) health in real time. By measuring the resulting impedance at the power converters' terminals at various grid nodes, a neural network-based classifier helps detect the grid's health condition and identify the potential fault-prone zones, along with the type and location of the fault type in the grid topology. For a faulty grid, a Naive Bayes and a support vector machine (SVM)-based classifiers are used to locate and identify the faulty type, respectively. A separate neural network-based regression model predicts the source power delivered and the loads at different terminals in a healthy grid network. The proposed concepts are supported by detailed analysis and simulation results in a simple four-terminal DC microgrid topology and a standard IEEE 5 Bus system.