Observer-based Sensor Fault Detection in Islanded AC Microgrids Using Online Recursive Estimation

Performance and security of power systems are threatened with different classes of faults. Each fault considering its place and severity has a detrimental effect on system stability and performance. This fact makes fault detection and control essential. Although the system may suffer from output degradation in this case, appropriate control methodologies take place to mitigate the adverse effects of faults and maintain stability. This paper investigates fault detection problem, in a well-established islanded AC microgrid benchmark, which its applications have been substantially developed thanks to microgrids’ superiority over traditional power systems. The proposed sensor fault detection approach is based on online recursive model estimation and observer-based residual generation. Using the estimated model, linear Kalman filters are designed to provide the residual signals, based on which a sequential change detection unit will decide whether the system is in normal operation mode or a fault has occurred.