Cyber-physical security framework for Photovoltaic Farms
- University of Georgia, Athens, GA (United States); University of Georgia
- University of Georgia, Athens, GA (United States)
With the evolution of PV converters, a growing number of vulnerabilities in PV farms are exposing to cyber threats. To mitigate the influence of cyber-attack on PV farms, it is necessary to study attacks' impact and propose detection methods. To meet this requirement, a cyber-physical security framework is proposed for PV farms. Data integrity attacks (DIAs) are studied on different control loops. As μPMU is gaining in popularity, a lower sampling rate of μPMU data is applied to develop a detection algorithm. We have evaluated two data-driven methods, which are support vector machine (SVM) and long short-term memory (LSTM). Lastly, the data-driven methods verify the feasibility of μPMU data in attack detection.
- Research Organization:
- University of Arkansas, Fayetteville, AR (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- EE0009026
- OSTI ID:
- 2341850
- Journal Information:
- 2020 IEEE CyberPELS (CyberPELS), Journal Name: 2020 IEEE CyberPELS (CyberPELS)
- Country of Publication:
- United States
- Language:
- English
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