Central Difference Kalman Filter Approach Based Decentralized Dynamic States Estimator for DFIG Wind Turbines in Power Systems

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

Renewable energy integrated complex power systems suffer from its intermittency and unpredictability. Dynamic States Estimators (DSEs) with high accuracy can provide useful information for controllers design. However, Doubly Fed Induction Generator (DFIG) is a highly nonlinear system, where non-linear system estimation approaches have to be adpoted. In this paper, we proposed a novel Central Differene Kalman Filter (CDKF) based Decentralized Dynamic States Estimaor for DFIG interconnected complex power systems. CDKF is derived based on Sterling's polynomial interpolation, which generates advanced sigma points for capturing statistical information. Due to the advent of Phasor Measurement Units (PMUs), the decentralized estimation becomes applicable. Successful operation of the proposed DSE is verified through MATLAB simulations on an IEEE standard test system, and a comparision has been given to Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF) and CDKF approches based DSE. The result shows that the proposed CDKF based DSE achieves the highest accuracy among them.

Original languageEnglish
Title of host publication2019 9th International Conference on Power and Energy Systems, ICPES 2019
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781728126586
DOIs
Publication statusPublished - Dec 2019
Event9th International Conference on Power and Energy Systems, ICPES 2019 - Perth, Australia
Duration: 10 Dec 201912 Dec 2019

Publication series

Name2019 9th International Conference on Power and Energy Systems, ICPES 2019

Conference

Conference9th International Conference on Power and Energy Systems, ICPES 2019
Country/TerritoryAustralia
CityPerth
Period10/12/1912/12/19

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