Kernel-based cumulative sum and differential cumulative sum approaches for resilient fault detection in LVDC microgrids

Abstract

The energy industry is rapidly shifting from conventional to smart grid systems, involving advancements in microgrids and power management systems. However, achieving desired outcomes with the existing grid setup poses significant challenges. To address the above issues, adopting a Low Voltage Direct Current (LVDC) distribution system provides successful operational advantages. The primary objective of this work is to improve the system's security by developing a more resilient fault detection scheme. The presented work has proposed a Kernel-based Cumulative Sum (K-CUSUM) and Kernel-based Differential Cumulative Sum (K-DCUSUM) technique which is designed to identify changes in data under fewer assumptions. Instead of using the classical Maximum Mean Discrepancy (MMD) technique, the K-CUSUM and K-DCUSUM methods use a non-parametric MMD testing framework to evaluate incoming data by comparing it to reference distribution samples. Further, a comprehensive analysis is undertaken on an LVDC test system, considering fault scenarios involving pole-to-pole and pole-to-ground configurations. This research encompassed the determination of fault location, evaluation of fault resistance, examination of noise introduction, and an exploration of the influence of photovoltaic (PV) penetration. Moreover, the proposed approach is tested for a standard LVDC Microgrid. The results are compared with other contemporary techniques and recent literature.