A Hybrid Estimation-Based Technique for Partial Discharge Localization

Abstract

This article demonstrates the use of five different methods to estimate partial discharge (PD) location in an oil insulation system from noisy measurements. The measurements are obtained from three ultrasonic sensors located in three different places. The sensors map the PD location utilizing a nonlinear model. The estimation techniques used in this article are the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the smooth variable structure filter (SVSF), the EK-SVSF, and the UK-SVSF. The last two filters use the combination of EKF or UKF with SVSF, respectively, to consider possible PD model uncertainty. The proposed integrated UK-SVSF algorithm achieves the following objectives. First, the use of the Kaman-based filter enhances the optimality of the filter to system dynamics and measurements noise. Second, the use of the UKF reduces the calculation complexity and errors by replacing the Jacobian calculation with statistical linearization. Finally, the use of the SVSF enhances the estimate's robustness to model uncertainty. The experimental results verify the claim that the PD location estimate with minimum error is achieved by the UK-SVSF.