Fault Location Method Research Articles

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods.

Traveling-wave-based Fault Location using Time Delays between Modal Components in Electrical Distribution Systems

Background: The distribution feeders usually include many laterals, too many sensors need to be installed to locate the fault positions in electrical distribution systems by using the traditional double-ended traveling-wave-based fault-location methods Objective: Fault location based on the time delays between the moments of the first wavefronts of zero-mode voltages and that of aerial-mode voltages arriving at one end of the distribution feeder. Methods: Large decomposition level in wavelet transformation is adopted in this paper to obtain the zero-mode velocity. Results: Both theoretical research and simulation experiments have proved that the proposed fault location method can greatly improve the accuracy of the fault location in the electrical distribution systems without many sensors. Conclusion: The proposed fault location method can provide one feasible way to locate the faults in the electrical distribution systems.

Signal-based fault location method using time and frequency domain

This research introduces an innovative approach to fault detection in high-voltage alternating current (HVAC) systems by integrating temporal and frequency domain characteristics of signals. It utilizes Traveling Wave (TW) detection and analysis, focusing on a precise window of just 100 microseconds surrounding the TW's arrival at the measurement point. Mathematical Morphology is applied to identify time-domain properties, while the Stationary Wavelet Transform is employed to extract frequency-domain features. Additionally, the integration of a Dynamic Mode Decomposition-based technique enhances TW detection accuracy. The efficacy of this approach is extensively assessed for fault site classification and regression tasks, demonstrating superior performance compared to utilizing time or frequency-domain features separately. The study also examines the method's resilience to noisy measurements and the sufficiency of training data. Comparative analysis against existing signal-based approaches within the IEEE 34 node system underscores its heightened accuracy. Furthermore, the proposed technique showcases minimal fault location estimate errors along the feeder length and competes favorably with slower phasor-based fault detection methods in performance. Regarding the distance of the fault, its proximity to the generator, transmission line, or load significantly influences the parameters of positive, negative, and zero sequence components. When a fault occurs, these parameters undergo distinct changes, serving as indicators of the fault's severity and location. If the fault occurs near the generator or substation, there may be alterations in the voltage and current magnitudes, affecting the sequence components accordingly. In the case of a fault in the transmission line, impedance changes can lead to variations in sequence parameters. Similarly, a fault near the load can cause disturbances in the voltage and current profiles, impacting the sequence components. Analyzing these changes in positive, negative, and zero sequence parameters during a fault provides valuable insights into the fault's severity and location, aiding in effective fault detection and mitigation strategies. Key Words – fault detection, high-voltage alternating current (HVAC) systems, Traveling Wave (TW), temporal domain, frequency domain, Mathematical Morphology, Stationary Wavelet Transform, Dynamic Mode Decomposition, fault site classification,

A Novel DWT-ERT-based Fault Location for Distribution Network

Background: A new DWT-ERT-based fault location method is suggested in the IEEE test feeder. Objective: The fault location approach in the distribution network has been proposed in this pa-per that utilizes the discrete wavelet transform (DWT) and ensemble regression tree (ERT). Methods: The fault location methodology has been validated by simulations conducted on an IEEE 13 bus node test feeder. Results: The results show that the suggested solution has low compute burden and memory re-quirements, and is unaffected by system and fault situations. Conclusion: In this study, the fault location approach for the distribution system employing DWT and ERT has been proposed.

A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors

A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability.

Flexible grounding system fault location method based on steady state projection

Flexible grounding system fault location method based on steady state projection

Study on Traveling Wave Fault Localization of Transmission Line Based on NGO-VMD Algorithm

To address the challenge of inaccurate fault location of variational mode decomposition (VMD) in practical engineering, due to poor choice of mode decomposition number K and quadratic penalty factor α, a traveling wave fault location method using Northern Goshawk optimization algorithm (NGO) to optimize VMD was proposed. First, the NGO algorithm is used to optimize VMD, and the optimal K and α are obtained. Secondly, the optimal parameters are inputted into VMD for fault signal decomposition, and the eigenmode components are obtained. Due to the difficulty of identification of the traveling wave head in the process of traveling wave propagation, Hilbert transform is used to determine the time of initial arrival of the traveling wave head at both ends of the line, and the fault location is precisely calculated by using the two-ended traveling wave fault detection formula. Finally, simulation experiments are carried out to verify the accuracy of the proposed location method, which shows that the proposed location method can locate the fault more accurately and has good engineering application value.

Application of a Centroid Frequency-Based Back Propagation Neural Network Fault Location Method for a Distribution Network Considering Renewable Energy Assessment

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m.

Low-voltage characteristic voltage based fault distance estimation method of distribution network

The traditional medium-voltage distribution network fault location method uses mainly the voltage and current measurements of the medium-voltage side, which results in problems such as high installation costs at the measuring points and complicated postoperation and maintenance work. Therefore, a fault location idea based on the distributed measurement of low-voltage side voltage is proposed in this paper. First, the characteristic voltage is adaptively selected according to the fault type. Second, the suspected fault section is determined by comparing the characteristic voltage amplitude of each measuring point. Third, the fault section is located using the section unit characteristic voltage drop defined for each suspected fault section. Finally, fault distance estimation is achieved based on the voltage difference matrix and characteristic voltage analysis. This method achieves accurate fault distance identification based on the distribution difference of the characteristic voltage of the low-voltage side under the fault state. This work provides a new economical and practical idea for determining the fault locations of distribution networks. The effectiveness of this method is evaluated by considering a 10 kV distribution network in Guangdong Province built in PSCAD/EMTDC.

A method for locating lightning strikes in distribution lines with multiple branches

A lightning strike fault location (FT) method for multi-terminal overhead lines based on 0-mode 1-mode wave velocity difference is proposed to address the shortcomings of current multi-terminal overhead line positioning methods that are difficult to identify due to multi-terminal GPS time synchronization and reflected traveling waves. Based on the wave velocity difference between 0-mode and 1-mode, single-end ranging is performed, and an FT judgment vector is formed based on the ratio of the ranging results of each monitoring terminal to the inherent length of the line. By analyzing the element features in the FT judgment vector, an FT criterion is proposed, and the FT results of the appropriate monitoring terminal are selected as the final FT. After PSCAD/EMTDC simulation verification, this method is suitable for complex multi-branch overhead lines, and can accurately locate the FT without being affected by time synchronization.

Deployability of a Probabilistic Earth Fault Location Method for MV Distribution Feeders

Deployability of a Probabilistic Earth Fault Location Method for MV Distribution Feeders

A Fault Location Method Based on Electromagnetic Transient Convolution Considering Frequency-Dependent Parameters and Lossy Ground

A Fault Location Method Based on Electromagnetic Transient Convolution Considering Frequency-Dependent Parameters and Lossy Ground

A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids

DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However, the reliable operation of DC microgrids relies on the early detection and location of faults to ensure an uninterrupted power supply. This paper aims to develop fast and reliable fault detection and location mechanisms for DC microgrids, thereby enhancing operational efficiency, minimizing environmental impact, and contributing to resource conservation and sustainability goals. The fault detection method is based on compressed sensing (CS) and Regression Tree (RT) techniques. Besides, an accurate fault location method using the feature matrix and long short-term memory (LSTM) model combination has been provided. To implement the proposed fault detection and location method, a DC microgrid equipped with photovoltaic (PV) panels, the vehicle-to-grid (V2G) charging station, and a hybrid energy storage system (ESS) are used. The simulation results represent the proposed methods’ superiority over the recent studies. The fault occurrence in the studied DC microgrid is detected in 1 ms, and the proposed fault location method locates the fault with an accuracy of more than 93%. The presented techniques enhance DC microgrid reliability while conserving renewable resources, vital to promoting a greener and more sustainable power grid.

A parameter-free fault location method for cross-bonding cable based on ranging equations

With the increasing application of long-distance high-voltage cables using the cross-bonding grounding method in urban power grids, the effectiveness and accuracy of the single-phase short-circuit fault location method for cross-bonded cables have gradually gained attention. To reduce positioning errors caused by inaccurate or unknown line parameters, a fault location method independent of line parameters is proposed. Firstly, the changes in sheath circulating current before and after the fault occurrence are analyzed, and based on these changes, a criterion for fault identification is established to determine the approximate range of fault occurrence. Secondly, considering the three-phase electromagnetic coupling of cross-bonded cables, an equivalent model for single-phase short-circuit faults in cross-bonded cables is established based on the double-π model. The along-line voltage and current are calculated by deducing electrical quantities at the ends of the cable. Then, the fault location equations are formulated based on the fault section, and the trust-region algorithm is employed to solve for fault distances and unit-length parameters of the cable in each equation group. Finally, a cable fault model is built using the PSCAD/EMTDC simulation platform, and the influence of different factors on the fault location method is analyzed. The results show that the fault location error of the proposed method is around 0.2 %, demonstrating high accuracy.

A Telegrapher’s equations-based implicit finite difference solution for fault location with current transformer saturation

This paper introduces a fault location method based on time analysis, utilizing synchronized data collected from both ends of the faulted line. The data is obtained either from Common Format for Transient Data Exchange (COMTRADE) files or Sampled Measured Value (SMV) of the IEC-61850 protocol. The method proposed in this study employs the implicit finite difference solution to the Telegrapher’s equations. In certain fault scenarios, such as when one or both transformers experience saturation, a narrow time window of data can be utilized to accurately pinpoint the fault location. The implicit solution method is unconditionally stable and therefore decouples the choice of the line discretization step from the sampling time interval, which is commonly required to maintain numerical stability of the explicit strategies for fault location. Furthermore, the numerical stability of the implicit process allows for devising a systematic search procedure that improves the accuracy of the calculated fault location, regardless of the transmission line length. By conducting numerical comparisons with an explicit time-domain fault locator and recent frequency-domain algorithms, the superiority of the proposed approach becomes evident. The results highlight the significant improvements achieved through the utilization of this method. Specifically, the results demonstrate that Telegrapher’s Equations implicit finite difference solution maintains accuracy under current transformer saturation, as it can locate the fault using data from a brief interval where the current transformer is not saturated.

Fault location in high-voltage direct-current grids based on panoramic voltage features and vision transformers

This paper presents a novel fault location method designed for high-voltage direct-current (HVDC) grids equipped with quick-action protections and circuit breakers. The method relies on a very short data window of single-ended voltage signals, which allows the acquisition of necessary measurements before the ultra-fast fault-isolation stage. Through fault analysis of the simulated four-terminal HVDC grid, it is observed that the fault distance induces a unique transient response in the voltage signal. However, due to the complex mathematical representation of loop components and the multiple fault factors involved, a data-driven strategy is adopted for fault distance estimation. Based on this strategy, four signal processing techniques, including Gramian angular field, recurrence plot, Markov transition field, and continuous wavelet transform, are used to extract panoramic voltage features. Additionally, four advanced lightweight variants of vision transformers (ViT), including MobileViT, EfficientFormer, Efficient-Model, and EdgeViT, are considered for constructing the regression model. The optimal combination of panoramic voltage features and regression models for configuring the locator is determined by statistical evaluation. The results of semi-physical experiments based on real-time digital simulators demonstrate that the proposed method, first, can accurately estimate the fault distance, second, possesses robustness against high transition resistance, low sampling frequency, and strong noise interference, and third, is practically feasible.

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points.

A double-ended third harmonic method for vegetation high impedance fault location in overhead distribution systems

Vegetation-related high-impedance faults (VHIFs) on overhead distribution systems can cause damage, maintenance issues, and even wildfires, impacting both utilities and ecosystems. Existing fault location techniques struggle to accurately locate VHIFs. In this paper, we propose a novel double-ended terminal location method based on the third harmonic component of the fault system. The proposed technique determines the distance of the VHIF by approximating the third harmonic voltage at the fault local, utilizing measurements from the remote terminal located at the far-end of the feeder. This method efficiently locates VHIFs with just two measurement terminals, cutting the need and cost of extensive sensor deployment. Moreover, the proposed method overcomes the limitations of traditional fault location techniques by using a third harmonic model of the faulted system, eliminating the need for complex mathematical models to approximate the fault behavior and voltage at the fault local. Simulation tests were conducted to validate the proposed method, demonstrating its effectiveness in accurately locating VHIFs. The technique achieved error rates below 1.6 %, making it a promising solution for real-world applications in power distribution systems. The ability to precisely locate VHIFs meets a proactive protection philosophy and contributes to improved power system protection and ensures uninterrupted power supply.

An Improved Fault Localization Method for Direct Current Filters in HVDC Systems: Development and Application of the DRNCNN Model

This paper focus on direct current (DC) filter grounding faults to propose a novel dilated normalized residual convolutional neural network (DRNCNN) fault diagnosis model for high-voltage direct current (HVDC) transmission systems. To address the insufficiency of the traditional model’s receptive field in dealing with high-dimensional and nonlinear data, this paper incorporates dilated convolution and batch normalization (BN), significantly enhancing the CNN’s capability to capture complex spatial features. Furthermore, this paper integrates residual connections and parameter rectified linear units (PReLU) to optimize gradient propagation and mitigate the issue of gradient vanishing during training. These innovative improvements, embodied in the DRNCNN model, substantially increase the accuracy of fault detection, achieving a diagnostic accuracy rate of 99.28%.