Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators
Conference
·
· 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA
- Iowa State University
In this paper, we propose linear operator theoretic framework involving Koopman operator for the data-driven identification of power system dynamics. We explicitly account for noise in the time series measurement data and propose robust approach for data-driven approximation of Koopman operator for the identification of nonlinear power system dynamics. The identified model is used for the prediction of state trajectories in the power system. The application of the framework is illustrated using an IEEE nine bus test system.
- Research Organization:
- Iowa State Univ., Ames, IA (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- OE0000876
- OSTI ID:
- 1572409
- Journal Information:
- 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, Conference: 2019 IEEE Power & Energy Society General Meeting, Atlanta, GA, USA, August 4-8, 2019
- Country of Publication:
- United States
- Language:
- English
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