Distributed Learning of Mode Shapes in Power System Models
We address the problem of distributed estimation of eigenvectors for power system models using online phasor measurements. The power system is considered to be divided into a set of non-overlapping areas, each of which is equipped with a local estimator. Online measurements of bus voltage and current phasors are first used to generate estimates of the generator states in each area using decentralized Kalman filters. Thereafter, these estimates are used for identifying a reduced-order model of the system in a completely distributed way by sharing state information between the estimators over a strongly connected communication graph. The identified model is then utilized to estimate its right eigenvectors over the same distributed graph. Results are validated using a 50-bus power system model with four areas.
- Research Organization:
- Univ. of Central Florida, Orlando, FL (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- EE0007998
- OSTI ID:
- 1826256
- Report Number(s):
- DOE-UCF-7998
- Journal Information:
- 2018 IEEE Conference on Decision and Control (CDC), Conference: 2018 IEEE Conference on Decision and Control (CDC)
- Country of Publication:
- United States
- Language:
- English
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