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Square-Root Sigma-Point Filtering Approach to State Estimation for Wind Turbine Generators in Interconnected Energy Systems

journal contribution
posted on 2021-06-01, 00:00 authored by Samson YuSamson Yu, Xinqi Fan, Tat Kei Chau, Hieu TrinhHieu Trinh, Saeid Nahavandi
The internal states of generators obtained by dynamic states estimation (DSE) may provide additional information for the control performances. However, conventional sigma-point Kalman filter (SPKF)-based DSE may experience the loss of positive definiteness and symmetricalness of state noise covariances. This article proposes three numerically stable square-root SPKF (SR-SPKF) algorithms and proposes a novel derivative-free SR-SPKF-based DSE framework to estimate the dynamic states for doubly fed induction generator (DFIG) wind turbines in an interconnected power network. While this article investigates the dynamic behavior of the power grid at a system-level, the DSE of DFIG is achieved in a decentralized manner, which is made possible by the use of phasor measurement units (PMUs) to acquire and transmit voltage and current phasors at DFIG terminal. By utilizing the SR-SPKF-based DSE framework and PMUs data, a comparison study is conducted for square-root unscented Kalman filter, square-root Cubature Kalman filter, square-root central difference Kalman filter, and their conventional versions. The computational burden, estimation accuracy, and mathematical capability of each filtering algorithm are compared and analyzed through simulation studies.

History

Journal

IEEE Systems Journal

Pagination

1 - 10

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscatawy, N.J.

ISSN

1932-8184

eISSN

2373-7816

Publication classification

C1 Refereed article in a scholarly journal