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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:2577-1647
DOI:10.1109/IECON43393.2020.9254668