An adaptive method for tuning process noise covariance matrix in EKF-based three-phase distribution system state estimation

Dragan Ćetenović, Aleksandar Ranković, Junbo Zhao, Zhaoyang Jin, Jianzhong Wu, Vladimir Terzija

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This paper proposes a new adaptive method for online tuning of process noise covariance matrix in the Extended Kalman Filter based three-phase distribution system state estimator. Specifically, a new form of exponential function is proposed for tuning the process noise covariance matrix, adapting it to the level of state changes. A new indicator derived from normalized innovations of the available real-time and virtual measurements is developed for tracking the level of state changes. The proposed method relies on measurements of the existing distribution networks, and therefore can be easily implemented in the advanced distribution management system. The method efficiently adapts process noise to system state variations and can deal with both quasi steady-state and unexpected sudden state changes caused by changes in the system topology, generation or demand. Comparison results with other state-of-the-art adaptive methods on the modified IEEE 37-bus and IEEE 123-bus distribution systems show that the proposed method achieves better accuracy under quasi steady-state condition while being more robust to unexpected sudden state changes.

Original languageEnglish
Article number107192
JournalInternational Journal of Electrical Power and Energy Systems
Volume132
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Adaptive tuning
  • Distribution network
  • Extended Kalman Filter
  • Measurement innovations
  • Process noise covariance matrix
  • State estimation

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