IoT-Based Data-Driven Fault Allocation in Microgrids Using Advanced µPMUs

Abstract

The ameliorations in high-precision phasor measurement units (μPMUs) and synchrophasor units have accommodated the distribution grid with peculiar visibility. Therefore, investigating the challenges of uncertainty consideration on precise fault detection in microgrids has become a new research milestone. This paper presents an effective data-driven stochastic method that justifies the adoption of only two μPMUs that are communicating under an IoT-based umbrella to detect and allocate irregularities in a microgrid. The proposed method has the ability to operate under a variety of case studies and scenarios including but not limited to the capacitor bank switching, distributed energy resources (DERs) diversity and high impedance fault occurrence, whilst considering the uncertainty in load, without installing individual sensors. Furthermore, a two-point estimate approach is utilized to model the uncertainties of the problem. Not only does the proposed stochastic framework benefit from the voltage magnitude measurement, but it also utilizes its angle in event allocation, which manifests better performance compared to ordinary voltage and current sensors. The simulation results on the proposed microgrid indicate the high accuracy and a sound success is obtained under a variety of case studies. The results show the high accuracy and applicable aspect of the proposed data-driven approach for fault allocation using a few μPMUs in the IoT context.