Partial Discharge Localization in Power Transformers Using Invariant Extended Kalman Filter

Abstract

This article proposes a novel invariant extended Kalman filter (IEKF), a recently modified version of the extended Kalman filter (EKF), to estimate partial discharge (PD) location in a transformer insulation system model. An acoustic signal measurement is utilized to localize the PD location. Unlike conventional EKF methods, where the correction term used to update the state is linearly proportional to the output error, the correction term of the proposed algorithm is independent of the output error, resulting in a fast response with a significant reduction in the estimation error. In contrast to the EKF, the proposed method can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the PD's dynamics, the IEKF will still sustain a reasonable performance. In contrast, conventional EKFs can easily diverge if a mismatch between the measurement model and the true measurement occurs. Experimental results are shown to verify the proposed method's performance compared to a recently published variant of the EKF.