Fault location using digital relay data

Accurate fault location on power transmission lines is critical for utilities to minimize downtime, reduce operational costs, and expedite system restoration. Traditional double-ended traveling wave-based fault locators have gained traction due to their high precision—often within two tower spans (~300 meters). However, these systems demand high-frequency sampling hardware (in the MHz range), intricate commissioning procedures, and detailed engineering configurations, which can be limiting. To overcome these challenges, we introduce a phasor-based fault location methodology that operates without the need for extensive settings. This approach utilizes both pre-fault and fault-time data and is compatible with synchronized or unsynchronized phasor measurements. When synchronization is absent, the method estimates the time alignment error using a derived design factor, which is either computed from synchronized data or configured during commissioning. The proposed technique has been validated through both laboratory experiments and field deployments, demonstrating reliable fault location accuracy. Notably, the solution is implemented on a protection relay platform using standard sampling rates (1 kHz), making it practical for widespread utility adoption.

Recent developments in power semiconductor devices have made High Voltage Direct Current (HVDC) transmission systems the most crucial technology for future power systems. Today several HVDC projects have been commissioned around the world. The primary concern for such an advanced HVDC grid is to follow standard grid codes to maintain the safety and stability of the electrical equipment during transient and dynamic circumstances. Although the HVDC system has been recognized as the most prominent technology to transmit electric power, some issues must be addressed, such as selective and quick detection of faults. For selective fault detection, it is essential to look into the several fault detection strategies that fortify the HVDC system with time-frequency signal processing techniques. This research uses a time frequency-based method to identify the different HVDC transmission line faults. The most suitable mother wavelet for various transient states is selected by analyzing fault voltage and current signals based on variance, standard deviation, and range parameters. An experimental setup of a thyristor-based HVDC transmission system has been developed in the MATLAB/Simulink environment, and multiple fault scenarios have been analyzed. The simulation outcomes reveal that the most effective mother wavelet for detecting DC line, symmetrical and unsymmetrical faults occurs at the 6th level of fault signal decomposition. By examining current and voltage signals, our work has determined the best mother wavelet for various faults. Using more than one mother wavelet to detect faults is more reliable than using a single mother wavelet to detect all faults. Additionally, it has been noted that each fault's voltage and current signals have two distinct mother wavelets.

Due to the complexity of software systems, bugs are inevitable. Software debugging is tedious and time consuming. To help developers perform this crucial task, a number of spectra-based fault localization techniques have been proposed. In general, spectra-based fault localization helps developers to find the location of a bug given its symptoms (e.g., program failures). A previous study by Parnin and Orso however implies that several assumptions made by existing work on spectra-based fault localization do not hold in practice, which hinders the practical usage of these tools. Moreover, a recent study by Xie et al. claims that spectra-based fault localization can potentially weaken programmers' abilities in fault detection.Unfortunately, these studies are performed either using only 2 bugs from small systems (Parnin and Orso's study) or synthetic bugs injected into toy programs (Xie et al.'s study), only involve students, and use dated spectra-based fault localization tools. Thus, the question whether spectra-based fault localization techniques can help professionals to improve their debugging efficiency in a reasonably large project is still insufficiently answered. In this paper, we perform a more realistic investigation of how professionals can use and benefit from spectra-based fault localization techniques. We perform a user study of spectra-based fault localization with a total of 16 real bugs from 4 reasonably large open-source projects, with 36 professionals, amounting to 80 recorded debugging hours. The 36 professionals are divided into 3 groups, i.e., those that use an accurate fault localization tool, use a mediocre fault localization tool, and do not use any fault localization tool. Our study finds that both the accurate and mediocre spectra-based fault localization tools can help professionals to save their debugging time, and the improvements are statistically significant and substantial. We also discuss implications of our findings to future directions of spectra-based fault localization.

Fault detection, classification, and location estimation are paramount in transmission lines to ensure uninterrupted power supply, enhance reliability, and safety. Faster and more accurate fault localization is critical for prompt repair and efficient power restoration, thus minimizing economic losses. This paper presents a novel Fault Detection Isolation and Localization (FDIL) scheme, a machine learning (ML)-based framework, to increase the accuracy of fault location (FL) prediction. The proposed framework utilizes the phasor voltage, current, and frequency data obtained from optimally placed Phasor Measurement Units (PMUs) for fault detection, isolation, and localization. This paper also proposes a hybrid deep learning-based approach using Bidirectional Long Short-Term Memory (Bi-LSTM) networks and an attention mechanism for FL prediction. Extensive simulations using the IEEE 9-bus system, with different types and locations of faults, are performed on the OPAL-RT simulator-based testbed to generate and train the ML models. The performance of the proposed scheme is evaluated by calculating the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) metric. The results demonstrate that the proposed approach achieves high accuracy in FL prediction, with an MAE of 0.0228 km, an MSE of 0.001, and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9894. Compared to the best-performing technique reported in the literature (CNN-LSTM, MAE = 0.211km), the proposed approach achieves an 89.2% reduction in localization error. The proposed scheme and model outperform the contemporary model, as reported in various literature, making it an effective solution for real-time fault monitoring in transmission systems.

Fault location information is critical for operating and maintaining transmission networks. Some of the challenges in calculating accurate fault location include fault resistance, zero-sequence mutual coupling, load, system nonhomogeneity, and transmission lines composed of multiple sections with considerably different characteristics. This paper presents a fully automated real-time fault location system that provides accurate fault location information for multiple transmission lines and makes the results available to system operators and maintenance personnel. The system performs the following tasks: • Retrieves all triggered event reports from relays and digital fault recorders (DFRs) automatically. With these reports, the system identifies the faulted transmission line(s) within the transmission network. • Determines the fault type and calculates the fault location and fault resistance using multiterminal fault location methods for different types of power lines, including overhead lines, underground cables, and composite lines that include both overhead line and underground cable sections. • Uses fault location methods that are accurate under fault resistance, parallel-line mutual coupling, system nonhomogeneity, and load. • Supports event reports with a fixed sampling rate or a variable sampling rate that is a multiple of the power system operating frequency. • Supports event reports generated by relays and DFRs connected to an IRIG-B time source. This paper compares fault location results calculated from a field event report that was obtained from an actual power system with actual fault location data.

This paper presents an improved hybrid approach for fault location in a series compensated transmission line. The proposed method uses one cycle post fault current and voltage samples. Thereafter features of faulty signal are extracted by wavelet transform. Best features are then selected by genetic algorithm based feature selection method and are fed as input to the extreme learning machine for fault location. The performance of the proposed method has been evaluated on a 300 km, 400 kV transmission line with thyristor controlled series capacitor placed at the middle. The proposed scheme has been tested for a wide variety of operating conditions like different fault inception angle, fault resistance, fault location and type of fault. Simulation result shows that the proposed method is quite fast and accurate for fault location in a series compensated transmission line.

An accurate fault location algorithm for transmission line based on R– L model parameter identification

With the development of microgrid technology, shortcircuit fault detection and location have become key challenges for the reliable operation of microgrids. To achieve accurate fault location in AC microgrids, a microgrid simulation model was constructed in MATLAB/Simulink in this study. The short-circuit faults under different distributed generation capacities were analyzed. Fault features were extracted using wavelet energy spectrum transformation, and a BP neural network was employed to train the fault location model. Simulation results indicate that the proposed method can achieve early fault detection and precise location, particularly demonstrating high accuracy in short-distance fault scenarios under grid-connected mode.

Aiming at the problem that the fault location accuracy of multi-terminal transmission line is not high, a fault location method based on multi-terminal traveling wave time difference is proposed. By analyzing the difference among multi-terminal traveling wave time difference in different fault locations, the multi-terminal traveling wave time difference matrix is established, which shows that the multi-terminal traveling wave time difference reflects the characteristics of different fault locations. F norm is used to analyze quantitatively the difference among the different fault location. By comparing the matrix F norm of each simulated fault point with the actual fault, the fault location is determined. Thus, the accurate fault location is calculated by the double-ended fault location PSCAD simulation result show that the proposed method is simple to implement and determine accurately the fault location. It has some certain practical vaule.

It has become increasingly challenging to ensure stability and maintain reliability in electrical power systems. Although significant research and technological advancements have been made, a fast and accurate fault detection and classification technique is still needed to ensure security and reduce power outages caused by faults. With a rising population, the demand for electrical power has surged, leading to an increase in power generation units. These units are interconnected through a complex power system network. Malfunctions in the power system can result in significant electrical disruptions, which in turn may lead to substantial economic losses. Therefore, uninterruptible and reliable power transmission is essential to support the country’s economic activities. To maintain stability and improve reliability, accurate fault localization is crucial. In real-life scenarios, faulty sections are isolated from healthy parts of the system with the help of relays and circuit breakers, though relay activation may incur a slight delay. Machine learning (ML) models are trained using available data, such as fault voltage and fault current. Currently, research is focused on fault detection and classification using Deep Learning (DL) and ML techniques.

Accurate fault location on EHV lines using both RBF based support vector machine and SCALCG based neural network

A method for accurate fault location on a distribution line with branches using the C-type of traveling wave signal and PNN is presented. The aim is to combine different location methods to improve the accuracy of fault location. In this method, the first step is determining the fault distance by C-type of traveling wave location method, then because branches which have the same distance to the busbar may be more than one, so using PNN neural network's pattern recognition feature to further determine the fault branch, which can achieve precise fault location. Both theoretical analyses and simulation show that this composite location method can be used to determine the single-phase-to-earth fault location accurately in the distribution network.

The structure complexity and the extended geographical area are among the factors that create challenges in accurate fault location and detection in distribution networks. The single‐phase to ground (SPG) faults are considered as most frequent reasons for distribution network interruption, which can threaten the network's reliability. Signal processing methods are usually used as common approaches to distinguish and locate faults in distribution networks. This paper proposes a novel scenario to select optimal wavelet packet transform (WPT) coefficients for SPG fault location and a method based on superimposed voltage energy to distinguish the faulty phase according to these coefficients. Furthermore, employing the energies of the superimposed voltages helps to minimize the effects of high resistance faults (HRFs) and load encroachment on the efficiency of the faulty phase detection part. Comparing the obtained results with other scenarios demonstrates the considerable efficiency of the proposed scenario. Finally, with the help of general regression neural networks (GRNN) as machine‐learning tools, a new algorithm is derived for detecting and locating the SPG fault and capacitor bank switching overvoltage (COV). Simulation results are implemented on the IEEE 34‐bus standard distribution network, demonstrating the efficiency and superiority of the suggested method.

Fault localization is the process of locating faulty lines of code in a buggy program. This paper presents a novel approach to automate fault localization by combining feature selection (a fundamental concept in machine learning) with mutual information (a fundamental concept in information theory). Specifically, we present a family of generalized entropies for computing generalized mutual information, which enables feature selection. The family generalizes well-known entropies, such as Shannon and Renyi entropies, and lays the foundation of a uniform entropy-based technique for fault localization. We perform an experimental evaluation of our approach using the Siemens suite of subject programs. Experimental results show that while using mutual information based on generalized entropies allows more accurate fault localization that traditional techniques, the specific entropies used do not have a significant impact on fault localization effectiveness.

Autotransformer (AT) traction network with bilateral power supply mode is an effective and economical electrified railway feeding scheme. However, due to the nonlinear relationship between measured reactance and distance, as well as the complex power flow distribution, the accuracy of fault location is facing severe challenges. This paper proposes a precise fault location method applicable to various fault scenarios in the AT traction network. By employing a generalized symmetrical components method, a composite sequence network model including four-sequence components is developed, and the boundary conditions are derived to describe the characteristics of various fault types. Furthermore, a fault-location algorithm is developed to determine the fault positions based on the busbar voltages and the feeder currents at trackside traction substations. The impact factors, such as the rail impedance and the parameter differences between contact wires and feeder wires, are investigated, and then a reasonable correction method is applied to improve the fault location accuracy. Finally, simulations, experimental setups, and field tests show that the proposed method can obtain accurate fault-location results, with an improvement of at least 29%. Certainly, this work has significant application prospects in fault analysis and location for electrified railways.