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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.
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Spatial modeling sensitivity analysis: Copula selection for wind speed dependence
As the adoption of wind energy as a key renewable energy source accelerates, precise power flow analysis becomes crucial for accurate power delivery forecasting. This paper addresses the inherent uncertainties in wind speed data at different wind farm locations by conducting a sensitivity analysis to assess wind farm pairs. The analysis accommodates various data sizes, namely, short, medium, and large, and diverse spatial relationships between wind farms. By leveraging National Renewable Energy Laboratory wind speed data from nine distinct wind farms, the dependence structure between wind farm pairs is modeled using copulas. This modeling takes both the wind speed knowledge level and the various spatial interplays among the wind farm pairs into consideration. The findings indicate an inverse proportionality between the strength of dependence and the distance separating the wind farm pairs.
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- Award ID(s):
- 1900462
- PAR ID:
- 10597527
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- AIP Advances
- Volume:
- 14
- Issue:
- 4
- ISSN:
- 2158-3226
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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