Resilient Dynamic State Estimation for Multi-Machine Power System With Partial Missing Measurements

• Author(s) / Creator(s)

  • Yi Wang, Yaoqiang Wang, Yonghui Sun, Venkata Dinavahi, Jun Liang, Kewen Wang

• Accurate tracking the dynamics of power system plays a significant role in its reliability, resilience and security. To achieve the reliable and precise estimation results, many advanced estimation methods have been developed. However, most of them are aiming at filtering the measurement noise, while the adverse affect of partial measurement missing is rarely taken into account. To deal with this issue, a discrete distribution in the interval [0,1] is introduced to depict mechanism of partial measurement data loss that caused by the sensor failure. Then, a resilient fault tolerant extended Kalman filter (FTEKF) is designed in the recursive filter framework. Eventually, extensive simulations are carried on the different scale test systems. Numerical experimental results illustrate that the resilience and robustness of the proposed fault tolerant EKF method against partial measurement data loss.

• Date created

  2023-06-01

• Subjects / Keywords

  • Sensor failure, partial missing measurements, extended Kalman filter, dynamic state estimation, power systems

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-gbbp-te17

• License

  Attribution-NonCommercial 4.0 International