Locally Weighted Ridge Regression for Power System Online Sensitivity Identification Considering Data Collinearity

Power system operations data are sometimes limited in a given space due to system collinearity. As such, the operations data recorded around an operating point of concern may be deficient or isotropically dispersed. Consequently, online sensitivity identification using ordinary regression methods is prone to large errors. In this paper, a locally weighted ridge regression method is proposed to overcome this problem. The norm-2 Tikhonov-Phillips regularization is integrated into the locally weighted linear regression. The integrated algorithm then has the ability to keep the online sensitivity identification stable if data are collinear while also accommodating the nonlinear and time-varying properties of the sensitivities. The mathematical derivation, online tuning, implementation, and practical considerations of the proposed method are presented. Its effectiveness is validated in a simulation system with operations data measured from real power systems.