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