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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Power Generation Maximization Control Framework for Ocean Current Turbine Farms
In this work, we propose a control framework for farms consisting of ocean current turbines (OCT). The ocean current turbine systems used in this farm are tethered to the ground of the ocean, and their depth can be adjusted online based on the maximum ocean current power available. To maximize the average power generated by the farm, the ocean current turbine wake interactions must be taken into account, and also each turbine in the farm should achieve these changes in the position reference with minimum control energy. Considering additional limitations such as keeping the tethering cables away from each other and avoiding collisions between the turbines, an advanced optimization framework is developed to achieve the maximum power generation in a specified region. Tracking of the reference trajectories by the ocean current turbine systems is achieved by model predictive control (MPC). A case study is presented to highlight the significant estimated improvement in the average energy generated by the farm using the proposed framework and control methodology.
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- Award ID(s):
- 1809404
- NSF-PAR ID:
- 10468468
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Format(s):
- Medium: X
- Location:
- Washington, DC, USA
- Sponsoring Org:
- National Science Foundation
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