Phasor state estimation from PMU measurements with bad data

Dongliang Duan, Liuqing Yang, Louis L. Scharf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

The phasor measurement units (PMU) are expected to enhance state estimation in the power grid by providing accurate and timely measurements. However, due to communication errors and equipment failures, some detrimental data can occur among the measurements. The largest residual removal (LRR) algorithm is commonly used for phasor state estimation with bad data. Here, we show that this method cannot guarantee correctness unless data redundancy is very abundant. We then establish the equivalence between the approaches of bad data removal and bad data estimation and subtraction. In addition, we propose two new algorithms by exploiting the sparsity of the bad data. All algorithms are tested by simulations and our projection and minimization (PM) algorithm provides the best performance.

Original languageEnglish (US)
Title of host publication2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Pages121-124
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 - San Juan, Puerto Rico
Duration: Dec 13 2011Dec 16 2011

Publication series

Name2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

Other

Other2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Country/TerritoryPuerto Rico
CitySan Juan
Period12/13/1112/16/11

Fingerprint

Dive into the research topics of 'Phasor state estimation from PMU measurements with bad data'. Together they form a unique fingerprint.

Cite this