State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems

Shenglong Yu, Kianoush Emami, Tyrone Fernando, Herbert Iu, Kit Wong

    Research output: Contribution to journalArticle

    42 Citations (Scopus)

    Abstract

    This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is performed using the Unscented Kalman Filter (UKF) with a bad data detection scheme. Such a state estimation scheme for a DFIG is important because not all dynamic states of a DFIG are easily measurable. Furthermore, the proposed state estimation technique is decentralized and the network topology of the entire power system is taken into consideration in the estimation process. In order to enhance the error tolerance and self-correction of the power system, bad data detection technique is implemented. A performance comparison with Extended Kalman Filter (EKF) is also discussed. © 1969-2012 IEEE.
    Original languageEnglish
    Pages (from-to)4935-4944
    JournalIEEE Transactions on Power Systems
    Volume31
    Issue number6
    DOIs
    Publication statusPublished - 2016

    Fingerprint

    Dive into the research topics of 'State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems'. Together they form a unique fingerprint.

    Cite this