Novel monitoring system for low-voltage dc distribution network using deep-learningbased disaggregation

Abstract

The deployment rate of distributed energy resources (DER) based on renewable energy has recently been increasing worldwide. Direct current (DC) power distribution has been proposed as an efficient approach for operating digital loads and DC-based renewable energy. DC distribution systems with DERs, however, are not commonly used in the real world. From the viewpoint of distribution system operators, it is important to identify the operation status of DERs for effective grid operation. In this article, a novel monitoring methodology for low-voltage DC (LVDC) distribution systems with DERs is proposed based on frequency-domain analyses. A deep-learning technology is applied to model the frequency characteristics of individual DERs. A case study considering two approaches was conducted using a photovoltaic generator, wind turbine, diesel generator, and energy-storage system installed in an LVDC testbed operated by KEPCO in Gochang, Jeolla-do, Korea. In the first approach, monitoring is performed with sensors installed near individual DERs. In the second approach, monitoring is performed with a single sensor in the distribution line, and the signal is disaggregated to identify the status of the individual DERs. The results show that the proposed methodology tracks the status of DERs with an accuracy of 98% and 95%, respectively, demonstrating the validity of the proposed methodologies.