Comparison Between Conventional and Post-Processing PMU-Based State Estimation to Deal with Bad Data

Research output: Research - peer-reviewArticle in proceeding

Abstract

Detection and analysis of bad data is one of the most important sector of static state estimation. This paper focuses on the comparison between a novel method for multi bad data detection and identification in PMU-based state estimation, namely post-processing PMU-based method for state estimation and the conventional PMU-based state estimation. To accomplish this object, available approaches in the PMU-based state estimation are overviewed, and their advantages and disadvantages are briefly explained. The largest normalized residual test is used to identify bad data. Then, phasor measurements are added by post-processing step in the second level of state estimation. The proposed algorithm of phasor measurements utilization in state estimation can prove that post-processing algorithm can detect and identify multi bad data in critical measurements, which it is not detectable by conventional methods. To validate simulations, IEEE 30 bus is implemented in PowerFactory and Matlab is used to solve proposed state estimation using post-processing of PMUs. Bad data is generated manually and added in PMU and conventional measurements profile. Finally, the location and analysis of bad data are available by result of largest normalized residual test.
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Details

Detection and analysis of bad data is one of the most important sector of static state estimation. This paper focuses on the comparison between a novel method for multi bad data detection and identification in PMU-based state estimation, namely post-processing PMU-based method for state estimation and the conventional PMU-based state estimation. To accomplish this object, available approaches in the PMU-based state estimation are overviewed, and their advantages and disadvantages are briefly explained. The largest normalized residual test is used to identify bad data. Then, phasor measurements are added by post-processing step in the second level of state estimation. The proposed algorithm of phasor measurements utilization in state estimation can prove that post-processing algorithm can detect and identify multi bad data in critical measurements, which it is not detectable by conventional methods. To validate simulations, IEEE 30 bus is implemented in PowerFactory and Matlab is used to solve proposed state estimation using post-processing of PMUs. Bad data is generated manually and added in PMU and conventional measurements profile. Finally, the location and analysis of bad data are available by result of largest normalized residual test.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Number of pages6
Place of PublicationMilan, Italy
PublisherIEEE Press
Publication dateJun 2017
ISBN (Print)978-1-5386-3917-7
DOI
StatePublished - Jun 2017
Publication categoryResearch
Peer-reviewedYes
Event2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Milano, Italy
Duration: 6 Jun 20179 Jun 2017

Conference

Conference2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
LandItaly
ByMilano
Periode06/06/201709/06/2017

    Research areas

  • Bad Data, Critical Measurement, Largest Normalized Residual, Phase Measurement unit, State Estimation

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