Dynamic State Estimation of Synchronous Machines Using Iterated Square-Root Cubature Kalman Filter and Synchrophasor Measurements

Authors

  • Teena Johnson Department of Electrical & Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India https://orcid.org/0000-0002-5714-2961
  • Sofia Banu Department of Electrical & Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India
  • Tukaram Moger Department of Electrical & Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India https://orcid.org/0000-0003-4176-5125

DOI:

https://doi.org/10.37256/jeee.2120232853

Keywords:

dynamic state estimation, synchronous machine, extended Kalman filter, unscented Kalman filter, cubature Kalman filter, iterated square-root cubature Kalman filter

Abstract

Power system dynamic state estimation is the first prerequisite for control and stability prediction under transient conditions. For a stable and reliable power system, it is crucial and helpful to have accurate, precise, and up-to-date information on the states of the synchronous machines- rotor angle and rotor speed deviation. This paper proposes an application of the Iterated Square-root Cubature Kalman filter (ISCKF) to estimate these main states of synchronous generators. The ISCKF method consists of two step modification - one is the square-root step modification of CKF and the next step is the addition of iterative approach to the square-root CKF method. To demonstrate the performance of the proposed approach during a three-phase short circuit fault, the simulation results, are compared with that of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF). The test systems considered are a single machine infinite bus (SMIB) system, an IEEE 9-bus system and a 19-generator 42-bus test system. The estimation accuracy of the rotor angle using ISCKF method is increased by 6.8–36.54% when compared to that of the CKF method. Similarly, the improvement in accuracy is 4.4–28.57% for estimation of speed deviation.

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Published

2023-06-16

How to Cite

(1)
Johnson, T.; Banu, S.; Moger, T. Dynamic State Estimation of Synchronous Machines Using Iterated Square-Root Cubature Kalman Filter and Synchrophasor Measurements. J. Electron. Electric. Eng. 2023, 2, 106–121.