To prevent the further escalation of multiple faults in transmission networks, which could lead to severe accidents, this article proposes an accurate fault location method for multiple faults using simulated virtual faults (VFs) traveling waves. The method is validated by comparing it with real faults wavefront sequences. Initially, the article analyzes the interactions and aliasing phenomena of traveling waves from different fault sources at measurement points based on their propagation characteristics. It also discusses the relative temporal and amplitude relationships between different fault wavefront sequences. Then, a matching method between VF wavefront sequences and real fault wavefront sequences is proposed, and a wavefront sequence matching ratio is formulated to quantify the location disparity between the VFs and real faults. Finally, the article introduces a multiple faults location mechanism and achieves accurate fault location for multiple faults using the dynamic virtual faults traveling wave location method. Verified through the PSCAD simulation platform, the proposed method is unaffected by errors in wave propagation speed, eliminates the need for terminal traveling wave measuring equipment, and can accurately locate up to three faults with a location error less than 100 m.

The fault location in transmission systems remains a challenging problem, primarily due to the fault location near the substation ends or the weak fault signals. In this study, an adaptive extended Kalman filter (EKF) based on the maximum likelihood (ML) is proposed to estimate the instantaneous amplitudes of the travelling waves. In this method, the EKF algorithm is used to estimate the optimal states (the clean travelling waves) with additive white noise while ML is used to adaptively optimise the error covariance matrices and the initial conditions of the EKF algorithm. Using the proposed method, the singularity points of travelling waves can be detected, and the exact arrival time of the initial wave head at the substations M and N can be easily yielded. Thus 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. Also, the proposed method has been tested with different types of faults, such as different fault locations, different fault resistances and different fault inception angles using ATP simulation. The accuracy of fault location using the proposed method has been compared with conventional wavelet transformation scheme.

Accurately detecting and classifying faults are essential for fault clearance and recovery of the system. One of the biggest challenges in fault diagnosis is dealing with fault class imbalances, where some fault types occur less frequently than others. The class imbalance makes the classifier prefer the majority class, which will cause the deterioration of classification performance. To solve the problem of serious class imbalances in fault natures, fault causes, and fault phases for the actual power system, this article presents a transmission line fault classification method based on the squeeze and excitation (SE)-Inception-residual network (ResNet) model with focal loss (FL). In this method, the FL is introduced to focus more on hard-to-classify faults by reducing the weight of easy-to-classify faults automatically. Also, the proposed model integrates the SE-Inception module and the SE-ResNet module, which can exploit the advantages of each to achieve better classification accuracy along with good computational efficiency due to the lighter network structure. To verify the effectiveness of the proposed method, various types of faults with unequal numbers are generated based on the 735-kV three-phase transmission line model, and three-phase signals are converted into 2-D time–frequency maps using continuous wavelet transform (CWT) for extraction of the deeper and higher level features. Experimental results show that the proposed method achieves 98.5% classification accuracy and above 92% F1-score, which indicates the improvement of classification performance in the case of serious class imbalance. Also, the effectiveness of this proposed method is verified by the real recording fault data. In addition, the comparisons with other convolutional neural networks (CNNs) and the cross-entropy (CE) demonstrate its superiority. The interpretability of the proposed networks for fault recognition is provided based on the gradient-weighted class activation mapping (Grad-CAM) visualization, and it reveals the inherent mechanism of its good classification performance from time–frequency features.

This study aims to address the challenge of fault location in distribution networks with closed-loop structures. We propose a comprehensive method that focuses on two aspects: traveling wave head calibration and fault location. By analyzing the propagation properties of hybrid lines traveling waves, we demonstrate the feasibility of optimizing wave speed using time difference measurements. To solve the time calibration problem of weak fault signals, we utilize the signal cumulative deviation curve to select the time window of fault mutation, and then develop a novel wide scale time window difference operator for accurate calibration. Finally, we use a positioning method based on dynamic virtual faults to optimize wave speed and accurately locate faults in both overhead and cable lines. We build an improved IEEE 118 node network using power system simulation software (PSCAD) for simulation verification, which shows that our proposed method can accurately calibrate the initial wavefront time of fault traveling waves and precisely locate the fault positions.

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).