IECON20_Power System Sensitivity Matrix Estimation by Multivariable Least Squares Considering Mitigating Data Saturation.pdf (758.54 kB)
Power System Sensitivity Matrix Estimation by Multivariable Least Squares Considering Mitigating Data Saturation
preprintposted on 2020-05-18, 16:26 authored by Yingqi LiangYingqi Liang, Junbo Zhang, Dipti Srinivasan
To data-driven estimate 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 memoryfading 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 estimation based on time-varying 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.
Email Address of Submitting Authoryingqi.firstname.lastname@example.org
Submitting Author's InstitutionDepartment of Electrical and Computer Engineering, National University of Singapore
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