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 onlinemeasured 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.