Noise Effect and Noise-Assisted Ensemble Regression in Power System Online Sensitivity Identification

Recently developed data acquisition equipment and data processing methods have ignited the possibility of power system online sensitivity identification (OSI). Despite the existing OSI algorithms, practical issues such as data collinearity and the noise effect on the identification algorithm must be considered to realize OSI in real-power systems. In this study, the negative and positive aspects of noise to OSI are first studied. Then, under the data collinearity condition and by making use of the positive aspects of noise, a noise-assisted ensemble regression method is proposed to simultaneously solve the data collinearity problem and manage the negative aspects of noise. Moreover, the proposed method is proven equivalent to one of the most effective measures, the norm-2 regularization method, to address the collinearity problem, and therefore provides satisfactory OSI results. The proposed method is tested in an 8-generator 36-node system with original operations data from a real-power system, and the results validate its effectiveness.