Measurement denoising using kernel adaptive filters in the smart grid

Abstract

State estimation plays an important role in the smart grid. Conventionally, noisy measurements are directly used for state estimation. Today, in the context of the smart grid, security becomes more important. False data or malicious data could be injected to compromise the smart grid system. In this paper, a measurement denoising module is proposed for denoising measurements and filtering out random false or malicious data ahead of state estimation. The measurement denoising module can suppress not only noises, but also random false or malicious data. Moreover, the emerging kernel adaptive filters are proposed to be applied to measurement denoising. Simulation results show that kernel adaptive filters perform better in denoising measurements and filtering out random false or malicious data.