Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation
Model-based and data-driven methods are combined to develop a hierarchical decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1496056
- Report Number(s):
- NREL/PO-5D00-73236
- Resource Relation:
- Conference: Presented at Operator Theoretic Methods in Dynamic Data Analysis and Control, 11-15 February 2019, Los Angeles, California
- Country of Publication:
- United States
- Language:
- English
Similar Records
A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation: Preprint
A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems