6533b82ffe1ef96bd12964ba
RESEARCH PRODUCT
Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems
Hamid Reza KarimiXiangyun QingXingyu WangYugang Niusubject
Consensus algorithmEstimationMathematical optimizationElectric power systemEngineeringControl theorybusiness.industryEnergy Engineering and Power TechnologyState (computer science)State informationKalman filterElectrical and Electronic Engineeringbusinessdescription
Abstract A decentralized unscented Kalman filter (UKF) method based on a consensus algorithm for multi-area power system dynamic state estimation is presented in this paper. The overall system is split into a certain number of non-overlapping areas. Firstly, each area executes its own dynamic state estimation based on local measurements by using the UKF. Next, the consensus algorithm is required to perform only local communications between neighboring areas to diffuse local state information. Finally, according to the global state information obtained by the consensus algorithm, the UKF is run again for each area. Its performance is compared with the distributed UKF without consensus algorithm on the IEEE 14-bus and 118-bus systems. The low communication requirements and high estimation accuracy of the decentralized UKF make it an alternative solution to the multi-area power system dynamic state estimation.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-02-01 | International Journal of Electrical Power & Energy Systems |