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Title: Cyber Threat Analysis Framework for the Wind Energy Based Power System
Wind energy is one of the major sources of renewable energy. Countries around the world are increasingly deploying large wind farms that can generate a significant amount of clean energy. A wind farm consists of many turbines, often spread across a large geographical area. Modern wind turbines are equipped with meteorological sensors. The wind farm control center monitors the turbine sensors and adjusts the power generation parameters for optimal power production. The turbine sensors are prone to cyberattacks and with the evolving of large wind farms and their share in the power generation, it is crucial to analyze such potential cyber threats. In this paper, we present a formal framework to verify the impact of false data injection attack on the wind farm meteorological sensor measurements. The framework designs this verification as a maximization problem where the adversary's goal is to maximize the wind farm power production loss with its limited attack capability. Moreover, the adversary wants to remain stealthy to the wind farm bad data detection mechanism while it is launching its cyberattack on the turbine sensors. We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on synthetic test cases.  more » « less
Award ID(s):
1657302 1929183
NSF-PAR ID:
10056669
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy - CPS '17
Page Range / eLocation ID:
81 to 92
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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