Distributed Multiple Fault Detection and Estimation in DC Microgrids with Unknown Power Loads

This paper proposes a distributed diagnosis scheme to detect and estimate actuator and power line faults in DC microgrids subject to unknown power loads and stochastic noise. To address actuator faults, we design a fault estimation filter whose parameters are determined through a tractable optimization problem to achieve fault estimation, decoupling from power line faults, and robustness against noise. In contrast, the estimation of power line faults poses greater challenges due to the inherent coupling between fault currents and unknown power loads, which becomes ill-posed when the underlying system is insufficiently excited. To the best of our knowledge, this is the first study to address this critical yet underexplored issue. Our solution introduces a novel differentiate-before-estimate strategy. A set of diagnostic rules based on the temporal characteristics of a constructed residual is developed to distinguish load changes from line faults. Once a power line fault is detected, a regularized least-squares method is activated to estimate the fault currents, for which we further derive an upper bound on the estimation error. Finally, comprehensive simulation results validate the effectiveness of the proposed methods.