Double‐ended travelling‐wave fault location based on residual analysis using an adaptive EKF

Abstract

This paper presents the estimated residuals for detecting the traveling wave front using an adaptive extended Kalman filter based on the maximum likelihood (EKF-ML), which uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. In some situations, such as faults close to the bus or close to zero inception angle, or faults with high fault resistances, the transient waves can become weak and even become lost in the noise, which makes the discrimination of the traveling wave front become more difficult. Aiming at this, residuals between the observed values and the estimated values using the adaptive EKF exhibit remarkable singularities, and can be used for exactly determining the wave front. Thus, the exact arrival time of the initial wave head can be determined and then the fault distance can be calculated precisely. The effectiveness of exacting mutation feature using the proposed method has been demonstrated by a simulated instantaneous pulse. And it has been tested with different types of faults using ATP simulation. Simulation results verify that the estimated residuals are highly sensitive to traveling wave front and less sensitive to modeling uncertainty (such as noise disturbance).