SUGAR-Enabled Intrusion Detection System for Electric Grid Cyber Threats Final Technical Report
The electric grid is the backbone of America’s economy, and its protection from emerging cyber threats is of the utmost importance. False data injection attacks (FDIAs) are a class of grid cyber attack where critical data, such as system measurements, are spoofed over the communication channel between grid devices and central control. During an FDIA the operator may make misinformed, erroneous decisions based on this spoofed data, resulting in total or partial collapse of the grid. The goal of Phase I R&D was to develop a physics-based software platform that, when embedded in a utility’s energy management system, offers an additional layer of protection against sophisticated cyber attacks that target control systems by tampering with grid measurements. Phase I R&D was devoted to developing a Python prototype of the FDIA detection layer for distribution grids. We developed three-phase models of measurement devices, including phasor measurement units (PMUs) and remote terminal units (RTUs), a novel state estimation analysis, and scripts to generate synthetic measurements for testing. We demonstrated successful three-phase state estimation runs with built-in anomaly detection based on synthetic system and measurement data. We also demonstrated the ability to rapidly simulate control actions on ISO-scale grid models and flag those that threaten grid reliability.
- Research Organization:
- Pearl Street Technologies, LLC
- Sponsoring Organization:
- USDOE Office of Science (SC), Engineering & Technology. Office of Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs
- DOE Contract Number:
- SC0019857
- OSTI ID:
- 1760161
- Type / Phase:
- SBIR (Phase I)
- Report Number(s):
- DOE-PST-19857-1
- Country of Publication:
- United States
- Language:
- English
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