An Asynchronous Decentralized Forecasting-Aided State Estimator for Power Systems

Sreenath Jayakumar Geetha, Saikat Chakrabarti, Ketan Rajawat, Vladimir Terzija

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Tracking the system states of an extensive power system is an onerous task as it estimates the power system states by processing a large measurement set. This paper proposes a new asynchronous decentralized forecasting-aided state estimator (ADFASE) to track the states of a power system. The networked power system is partitioned into smaller systems, which processes the local measurements in parallel to estimate system states of reduced dimensionality; thereby reducing the computational complexity. The subsystems then communicate the local information to neighboring subsystems, which collate the incoming information to obtain near-optimal estimates. The decentralized topology eliminates the need for any centralized infrastructure. Each processor operates in an asynchronous manner, and the information is communicated to neighboring subsystems as and when the information is processed. The information received by the subsystems are assimilated asynchronously and does not need any prior synchronization between the nodes. The performance of the proposed estimator is evaluated using IEEE 30 and 118-bus systems.

Original languageEnglish
Article number8630647
Pages (from-to)3059-3068
Number of pages10
JournalIEEE Transactions on Power Systems
Volume34
Issue number4
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

Keywords

  • Decentralized state estimation
  • extended information filter
  • extended Kalman filter (EKF)
  • forecasting-aided state estimator (FASE)
  • parallel algorithm

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