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Robust estimation method for power system dynamic synchronization with sensor gain degradation.

Yi Wang1, Jiawei Zhang1, Yaoqiang Wang1

  • 1School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.

ISA Transactions
|November 19, 2024
PubMed
Summary

This study introduces a robust method to estimate power grid synchronization dynamics even with sensor failures. The novel modified fault-tolerant extended Kalman filter (MFTEKF) ensures accurate monitoring of frequency, voltage, and magnitudes.

Keywords:
Extended Kalman filterPower gridRobust state estimationSynchronized measurements

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Signal Processing

Background:

  • Real-time power system synchronization estimation is crucial for operational safety.
  • Sensor gain degradation due to failures or interference compromises estimation accuracy.

Purpose of the Study:

  • To develop a dependable method for dynamic estimation in power grid synchronization under sensor gain degradation.
  • To monitor and track frequency, voltage phase angles, and magnitudes accurately.

Main Methods:

  • Characterized random sensor gain degradation using a discrete distribution [0,1].
  • Established a state-space model for sensor gain degradation.
  • Developed a novel modified fault-tolerant extended Kalman filter (MFTEKF) within a recursive estimator framework.

Main Results:

  • The proposed MFTEKF demonstrated robust performance in dynamic estimation.
  • Accurate monitoring and tracking of power grid dynamic characteristics were achieved.
  • Experimental results validated the effectiveness of the MFTEKF.

Conclusions:

  • The MFTEKF provides a reliable solution for power grid synchronization estimation amidst sensor gain degradation.
  • This approach enhances the safety and operational control of power systems.
  • The developed filter effectively addresses challenges posed by real-world sensor imperfections.