Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm

Abstract

Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers' side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy. INDEX TERMSDistributed generation; Discrete Kalman filter; Fault detection; Hardware in the loop; Microgrids protection; Mathematical morphology.

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

“…The rationale behind the choice of the EKF for fault detection in MVDCDN lies in its ability to effectively handle nonlinear and time-varying dynamic systems, characteristics often inherent in complex power distribution networks. Unlike traditional Kalman filters [26] [27], the extended version accommodates nonlinearities by linearizing system dynamics around an estimate, making it suitable for modeling the diverse behaviors of MVDCDN components under varying operating conditions and fault scenarios. Maintaining the stability and dependability of the MVDCDN relies heavily on the EKF's real-time fault identification, a recursive estimating technique critical for rapidly mitigating faults.…”