Real-Time Simulation of a Resilient Control Center for Inverter-Based Microgrids

TL;DR

This paper tackles the vulnerability of distribution-management-system algorithms in fully inverter-based microgrids to false data injection (FDI) attacks by implementing a real-time, hardware-in-the-loop testbed. It introduces a gated recurrent unit (GRU) detector that ingests five measurements to predict the grid breaker state and detects discrepancies with received breaker data to mitigate attacks in real time, achieving a reported accuracy of $94.91\%$. The approach is validated on a RTDS-based microgrid testbed comprising PV, BESS, and controllable loads, in both grid-connected and islanded operating modes, demonstrating mitigation of outages and extended resilience of critical loads. The work contributes a practical cyber-resilience pathway for DMS in inverter-based microgrids and provides a platform for evaluating other cyber-attack scenarios on PV and BESS components.

Abstract

The number of installed remote terminal units (RTU) is on the rise, increasing the observability and control of the power system. RTUs enable sending data to and receiving data from a control center in the power system. A distribution grid control center runs distribution management system (DMS) algorithms, where the DMS takes control actions during transients and outages, such as tripping a circuit breaker and disconnecting a controllable load to increase the resiliency of the grid. Relying on communication-based devices makes the control center vulnerable to cyberattacks, and attackers can send falsified data to the control center to cause disturbances or power outages. Previous work has conducted research on developing ways to detect a cyberattack and ways to mitigate the adverse effects of the attack. This work studies false data injection (FDI) attacks on the DMS algorithm of a fully inverter-based microgrid in real time. The fully inverter-based microgrid is simulated using an RTDS, an amplifier, an electronic load, a server, a network switch, and a router. The DMS is integrated into the server codes and exchanges data with RTDS through TCP/IP protocols. Moreover, a recurrent neural network (RNN) algorithm is used to detect and mitigate the cyberattack. The effectiveness of the detection and mitigation algorithm is tested under various scenarios using the real-time testbed.

Figures (10)

• Figure 1: NovaCor and PB5 racks of the RTDS setup.
• Figure 2: A four-quadrant amplifier and electronic loads.
• Figure 3: Study microgrid.
• Figure 4: Data exchange path of the testbed.
• Figure 5: A GRU unit: (a) in the time domain (b) gates structure of a GRU unit.