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).

A Novel Traveling Wave Fault Location Method for Transmission Network Based on Directed Tree Model and Linear Fitting

“…The location of the fault point is the intersection of two fitted straight lines. According to the Equations (13) and (21), the fault distance lEf2 = 39.52311km. The location error of this case is 0.010%.…”

“…Compared with the single-ended method, the currently used two-terminal traveling wave location method is not affected by various reflected waves and refracted waves, because it only needs to capture the first fault traveling wave head that arrives both ends of the transmission line [12][13][14]. In some existing two-terminal traveling wave location methods, generally only fault information is considered on one or both sides of the line [15][16][17].…”

Robust SCKF Filtering Method for MINS/GPS In-Motion Alignment

“…In order to handle with the strong nonlinear system, several filtering methods based on the Kalman filter have been proposed, such as the extended Kalman filter (EKF) [ 14 , 15 ], unscented Kalman filter (UKF) [ 16 , 17 , 18 ], Gauss–Hermite quadrature filter (GHQF) [ 19 , 20 ], sparse grid quadrature filter (SGQF) [ 21 , 22 , 23 ], and cubature Kalman filter (CKF) [ 24 , 25 ]. The EKF approximates the nonlinear model to a linear model through Taylor expansion to deal with nonlinear problems.…”

Improved square root adaptive cubature Kalman filter

“…Owing to the practicability and flexibility, non-linear filters have been widely studied in recent decades [1][2][3][4][5][6][7][8][9][10][11][12]. Motivated by the solution of intractable numerical integrals within the filter framework, various non-linear filters are proposed.…”

“…Motivated by the solution of intractable numerical integrals within the filter framework, various non-linear filters are proposed. The typical filters are the extended Kalman filter (EKF) [1,2], unscented Kalman filter (UKF) [3][4][5], Gauss-Hermite quadrature filter (GHQF) [6,7], sparse grid quadrature filter (SGQF) [8][9][10] and cubature Kalman filter (CKF) [11,12].…”