Power System Sensitivity Matrix Estimation by Multivariable Least Squares Considering Mitigating Data Saturation

To online estimate the power system sensitivity matrix considering mitigating data saturation, a series of multivariable least-squares (MLS) algorithms are proposed and compared, including the ordinary MLS (OMLS), the weighted MLS (WMLS), the memory-limited OMLS (ML-ORMLS), the memory-limited WRMLS...

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Vydáno v:IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society s. 1676 - 1683
Hlavní autoři: Liang, Yingqi, Zhang, Junbo, Srinivasan, Dipti
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 18.10.2020
Témata:
data saturation
multivariable regression
power system sensitivity matrix estimation
recursive least squares
ISSN:2577-1647
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Abstract To online estimate the power system sensitivity matrix considering mitigating data saturation, a series of multivariable least-squares (MLS) algorithms are proposed and compared, including the ordinary MLS (OMLS), the weighted MLS (WMLS), the memory-limited OMLS (ML-ORMLS), the memory-limited WRMLS (ML-WRMLS), and the memory-fading ML-WRMLS (MF-ML-WRMLS). Considering enhancing computational efficiency and accuracy by mitigating data saturation, the last three of them are specifically derived for sensitivity matrix online estimation using online-measured data. The effectiveness of the presented algorithms is verified and compared in the Nordic 32 system for voltage sensitivity matrix estimation. The results illustrate the prime algorithm in practice.
AbstractList To online estimate the power system sensitivity matrix considering mitigating data saturation, a series of multivariable least-squares (MLS) algorithms are proposed and compared, including the ordinary MLS (OMLS), the weighted MLS (WMLS), the memory-limited OMLS (ML-ORMLS), the memory-limited WRMLS (ML-WRMLS), and the memory-fading ML-WRMLS (MF-ML-WRMLS). Considering enhancing computational efficiency and accuracy by mitigating data saturation, the last three of them are specifically derived for sensitivity matrix online estimation using online-measured data. The effectiveness of the presented algorithms is verified and compared in the Nordic 32 system for voltage sensitivity matrix estimation. The results illustrate the prime algorithm in practice.
Author Zhang, Junbo
Srinivasan, Dipti
Liang, Yingqi
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  surname: Liang
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  givenname: Junbo
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  fullname: Zhang, Junbo
  email: epjbzhang@scut.edu.cn
  organization: South China University of Technology,School of Electrical Power,Guangzhou,China
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  givenname: Dipti
  surname: Srinivasan
  fullname: Srinivasan, Dipti
  email: dipti@nus.edu.sg
  organization: National University of Singapore,Department of Electrical and Computer Engineering,Singapore,Singapore
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Snippet To online estimate the power system sensitivity matrix considering mitigating data saturation, a series of multivariable least-squares (MLS) algorithms are...
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StartPage 1676
SubjectTerms data saturation
multivariable regression
power system sensitivity matrix estimation
recursive least squares
Title Power System Sensitivity Matrix Estimation by Multivariable Least Squares Considering Mitigating Data Saturation
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