A Fast Data-Driven Topology Identification Method for Dynamic State Estimation Applications
This paper proposes a fast topology identification method to avoid estimation errors caused by network topology changes. The algorithm applies a deep neural network to determine the switching state of the branches that are relevant for the execution of a dynamic state estimator. The proposed technique only requires data from the phasor measurement units (PMUs) that are used by the dynamic state estimator. The proposed methodology is demonstrated working in conjunction with a frequency divider-based synchronous machine rotor speed estimator. A centralized and a decentralized approach are proposed using a modified version of the New England test system and the IEEE 118-bus test system, respectively. The numerical results in both test systems demonstrate the reliability and the low computational burden of the proposed algorithm
Year of publication: |
[2022]
|
---|---|
Authors: | Gotti, Davide ; Ledesma Larrea, Pablo ; Amaris, Hortensia |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Saved in favorites
Similar items by person
-
A multiobjective approach for reactive power planning in networks with wind power generation
Alonso, Monica, (2012)
-
Coordinated reactive power management in power networks with wind turbines and FACTS devices
Amaris, Hortensia, (2011)
-
Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms
Alonso, Monica, (2014)
- More ...