Line Fault Location Research Articles

A physics-informed data-driven fault location method for transmission lines using single-ended measurements with field data validation

Data driven transmission line fault location methods have the potential to more accurately locate faults by extracting fault information from available data. However, most of the data driven fault location methods in the literature are not validated by field data for the following reasons. On one hand, the available field data during faults are very limited for one specific transmission line, and using field data for training is close to impossible. On the other hand, if simulation data are utilized for training, the mismatch between the simulation system and the practical system will cause fault location errors. To this end, this paper proposes a physics-informed data-driven fault location method. The data from a practical fault event are first analyzed to extract the ranges of system parameters such as equivalent source impedances, loading conditions, fault inception angles (FIA) and fault resistances. Afterwards, the simulation system is constructed with the ranges of parameters, to generate data for training. This procedure merges the gap between simulation and practical power systems, and at the same time considers the uncertainty of system parameters in practice. The proposed data-driven method does not require system parameters, only requires instantaneous voltage and current measurements at the local terminal, with a low sampling rate of several kHz and a short fault time window of half a cycle before and after the fault occurs. Numerical experiments and field data experiments clearly validate the advantages of the proposed method over existing data driven methods.

Traveling wave fault location for AC transmission lines: an approach based on the application of EMD and Teager energy operator

Traveling wave fault location for AC transmission lines: an approach based on the application of EMD and Teager energy operator

Research on New Energy Transmission Line Fault Localization Method Based on APSVMD-NTEO

Abstract Given the new energy transmission line fault, traveling wave head detection difficulties, wave speed uncertainty, positioning errors, and other problems, this paper proposes a new Teager energy operator based on adaptive parameter selection variational mode decomposition (APSVMD) and new energy transmission line fault location method (NTEO). The envelope fuzzy entropy is introduced to select the key parameters of SVM, and the sampling range of the Teager energy operator (TEO) is improved to effectively improve the capture accuracy of the wave head. In terms of fault location, by improving the double-ended traveling wave method, the uncertainty of wave speed and the effect of actual line length error is eliminated, and the ranging error is reduced.

Partially coupled transmission line fault location using single-ended measurements

Accurate fault location technology can not only speed up fault search but also enhance the power supply reliability. This paper introduces a single-ended fault location framework for single line to ground faults, taking into account the effects of partially coupled lines. The proposed framework relies only on local measurements. Within the proposed framework, the two methods proposed in this paper are based on the impedance line model and the fully distributed parameter line model, respectively. The impedance model based fault location method does not require iteration and has no risk of divergence, while the method based on the fully distributed parameter model has higher accuracy. The estimated results of the impedance-based method can be used as the initial values of the solution of the distributed parameter model based method. The proposed methods are validated through a number of testing cases, encompassing various fault locations, resistances, and coupling ratios.

UHVDC transmission line diagnosis method for integrated community energy system based on wavelet analysis

With the large-scale development of renewable energy power, China has faced with the challenges of the reverse regional distribution of wind and solar resources and power load, as well as the intermittency and randomness of renewable energy power. Therefore, China is vigorously developing ultra-high voltage direct current (UHVDC) transmission technology to solve the problem of insufficient flexibility caused by the uncertainty of renewable energy and the fluctuation of multi-energy loads in integrated community energy systems. UHVDC plays an increasingly pivotal role in the west-east transmission system in China’s power system due to its high transmission capacity and long transmission distance. Once the fault occurs in the ultra-high voltage direct (UHVD) transmission line, quick and accurate fault location identification is of great significance. Hence, this paper proposes a UHVDC transmission line diagnosis method based on wavelet analysis for integrated community energy systems. Wavelet transform (WT) is used to decompose the transient signal on a multi-scale, and then power systems computer-aided design (PSCAD) software is utilized for simulation calculation to obtain the singular spectrum entropy of each layer and facilitate wavelet transformations for signal denoising with advanced tools such as MATLAB. The prediction results can distinguish outside the rectification side fault, within the rectification side fault, and outside the inverter fault with an accuracy of 100%. A large number of simulations demonstrate that combining singular spectrum entropy with support vector machines (SVM) has emerged as a robust technique for integrated community energy systems, suggesting its potential as a standard method in UHVDC transmission line diagnosis. This study is of significant reference for realizing the complementarity of multiple types of power supply and ensuring a reliable power supply.

مرحل حماية المسافة وتأثير MOV على مناطق المرحل بإستخدام الماتلاب

Transmission lines are an important part of the power system. Transmission lines are used to transmit power and are prone to many breakdowns. Therefore, protection relays are used to protect transmission lines. The purpose of the protection system is to isolate the faulty part from the healthy part, because large currents generated by faults may cause damage to electrical equipment. One of the protection relays is the distance relay and is mainly used in the transmission line. Sometimes these relays are used for backup protection. Distance relays to determine impedance need voltage and current. Transmission lines are usually protected by a distance protection relay. Distance relays are of the high-speed class and can provide transmission lines. Protection distance relays that use impedance measurements to determine the presence and location of faults, are "fooled" by a series capacitor mounted on the line, when the presence or absence of a capacitor in the fault circuit is not known in advance. This is because the capacitor cancels or compensates for some of the line inductance, so the relay may sense a fault in its first region when the fault is actually in the second or third region of the protection. Similarly, Zone one fault can be considered reverse faults. In fact, this can cause some costly operating errors. Series compensated lines are protected from overvoltage by metal-oxide-varistors (MOVs) connected in parallel with the capacitor bank. The nonlinear characteristics of MOV devices add complexity to fault analysis and distance protection operation. During faults, the impedance of the line is modified by an equivalent impedance of the parallel MOV/capacitor circuit. Worth noting is a method that determines the L and C string values of the font and the MOV effect at the time of the error. This is done by analyzing the synchronous and sub-synchronous content of the voltage and current signals separately which provides enough information to calculate the L and C series of the line. In this paper, the transmission line impedance Z, Z(f) as a function of frequency has been calculated and plotted by the sequence characteristics and the studied MOV effect on line protection and fault location. Proper operation of the distance protection relay is also shown, and its effect on relay regions. Keywords: Distance protection relay, transmission line, zones of relay, MOV.

New energy transmission line fault location method based on Pearson correlation coefficient

The access of new energy power source makes the traditional transmission line structure become complex, and due to the influence of power electronic device control strategy, the fault characteristics have been fundamentally changed, resulting in the traditional transmission line fault localization method can not be applied to the new energy sending line. To address the above problems, this paper analyzes the transient current characteristics of different power supply faults, and learns that there are obvious differences in the transient currents on both sides of the fault point inside and outside the transmission line area, and then proposes a new energy transmission line fault localization method based on the Pearson correlation coefficient, which determines the fault location by calculating the correlation coefficients of the fault waveforms of the neighboring monitoring points. Finally, comparative experiments are carried out under different fault types and fault locations to further verify the applicability of the proposed method.

New energy grid-connected line fault localization method based on improved VMD-MUSIC

In view of the limitations of the traditional double-ended traveling wave ranging method in new energy grid-connected transmission lines and the inability to effectively extract the deep characteristics of the traveling wave intrinsic frequency. This paper proposes a new energy grid-connected line fault localization method based on the fusion of Variational Mode Decomposition (VMD) and Multiple Signal Classification (MUSIC). Firstly, the fault traveling wave signal is decomposed into multiple intrinsic modal components by improving VMD; then, the spectrograms under the corresponding modes are obtained by using MUSIC to realize the accurate extraction of the intrinsic frequency of the traveling wave; Finally, the fault distance is calculated based on the relationship between the fault distance at the monitoring points at each end and the known line length. Simulation results show that the method can overcome the influence of spectral aliasing on the extraction of the principal component of the intrinsic frequency, and can accurately determine the fault interval, and the localization results are not affected by the fault distance, the fault type, and the fault resistance, which has good adaptability and high localization accuracy.

VSC-HVDC transmission line fault location based on transient characteristics

VSC-HVDC transmission line fault location based on transient characteristics

Line fault location of flexible DC distribution network based on adaptive noise empirical mode decomposition

A fault location method for flexible DC distribution network lines is proposed to address the issues of insufficient decomposition in fault signal extraction and the complexity of traveling wave head detection. The method combines adaptive noise empirical mode decomposition (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN is used to adaptively denoise the line mode signal and obtain the optimal modal component to overcome modal aliasing and insufficient decomposition in signal separation. Then, the reach time of the fault traveling wave is determined by using TEO. Ultimately, the fault location is accurately determined by employing both ends of the traveling wave ranging technique. Through the combination of software PSCAD and MATLAB, the simulation experiment of the line fault is carried out. This means that the method can quickly and precisely identify the location of faults in a system.

Automated monitoring of high-voltage transmission lines based on machine vision

Abstract This paper takes the automated monitoring of high-voltage transmission lines as the entry point and constructs an automated monitoring model of high-voltage transmission lines based on machine vision. Through the transmission equipment recognition algorithm to identify the type and location of the equipment in the inspection image, using image processing methods for the collected high-voltage transmission line image color image grayscaling and grayscale image stretching, and then grayscale image smoothing and segmentation, so as to achieve the identification of the location of high-voltage transmission line fault location. Simulation test for image processing and transmission equipment identification methods, and then carry out automated monitoring experiments with high-voltage transmission line conductor stranding as an example to verify the feasibility and reliability of the model in this paper. The results show that for the automatic detection of individual conductor strand breakage, the method of this paper detects the number of strand breakage is only 1-2 less or more than the actual number of strand breakage; there is a certain difference, but basically in line with the actual situation, and the method of this paper, on average, detects the efficiency of the method is faster than the traditional method by 5.69~7.18 s/m. The research to enhance the quality of automated monitoring of high-voltage power transmission lines has a certain application value.

Retracted: Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Retracted: Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Eliminating the Dependence of GPS or Communication Latency Estimation in Traveling Wave Based Double-Terminal Fault Location

Double-terminal traveling wave-based fault location is one of the most reliable and accurate technologies for power transmission line fault location. However, existing methods require a global positioning system (GPS) or accurate communication latency estimation to synchronize data from the two terminals. In this study, we propose an innovative double-terminal traveling wave-based fault location method that is independent of either GPS or communication latency estimation. The basic principle of the proposed method is that the arrival time difference between initial surges could be roughly calculated through the angle of pre-fault phasors by artificially assigning surge arrival points as the time references. Therefore, an approximate fault distance is obtained. The error could be reduced by detecting the fault-point-reflected surge. Extensive simulation experiments were performed using PSCAD/EMTDC software to assess the performance of the proposed algorithm. The results showed that the proposed method is applicable to different fault circumstances and independent of GPS synchronization and communication latency estimation while maintaining accuracy close to those of state-of-the-art algorithms. Finally, hardware experiments were performed to assess the validity and practicability of the proposed method.

PMU placement for fault line location using neural additive models—A global XAI technique

In recent years, machine learning (ML) techniques have shown impressive results in many power system applications, including line fault location using phasor measurement units (PMU). Despite this success, such techniques may be hard to use in real life scenarios, if their results are not well understood by power system experts. In this light, this work proposes to use a global Explainable Artificial Intelligence (XAI) model for the problem of line fault location. Using such a model allows the user to examine which bus measurements are used for classifying different line faults, increasing user understanding and trust. Another benefit of this technique is that it provides a method for PMU placement in cases where the system is partially observable. The XAI method used is a Neural Additive Model (NAM), which provides a global explanation. Using this model and a novel evaluation technique where norm weights in the model are used to determine which bus measurements are most impactful, the most important measurements are determined, allowing for better PMU placement. This technique is compared to existing methods and matches benchmark performance while being computationally cheaper.

A novel fault location method based on traveling wave for multi-branch distribution network

In this paper, a novel fault location method is proposed for multi-branch distribution network based on traveling wave. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Teager energy operator (TEO) is used to detect the arrival time of traveling waves. Firstly, according to the signal processing ability of the ICEEMDAN method, the ICEEMDAN method is used to decompose the sampled signals when the fault occurs, and extract the high-frequency component of the sampled signals. Then, the TEO is used to extract the abrupt characteristics of high-frequency modal components. And the first maximum mutation point of the signal is regarded as the initial fault traveling wave signal. In addition, considering the transmission characteristic of fault traveling wave signal in multi-branch lines of distribution network, the difference value characteristic of branches is defined. And the recognition principle and the fault location method of multi-branch lines are established to realize the accurate location of distribution network line faults. Simulation results show that the proposed ICEEMDAN-TEO method can effectively detect the fault traveling wave signals, and has a better detection performance than CEEMDAN-TEO and HHT methods. The proposed fault location method can effectively locate fault positions and reduce location errors.© 2017 Elsevier Inc. All rights reserved.

Fault location in hybrid transmission lines on the basis of VMD energy entropy and SVM

Finding the head of a traveling wave is challenging because an overhead line-cable hybrid transmission line’s wave impedance is not constant. Because of this, it is difficult to measure the hybrid transmission line precisely. This leads to the proposal of a hybrid transmission line fault location algorithm, which combines VMD energy entropy for training support vector machines and traveling wave law graphs. The VMD energy entropy is first used to train SVM to identify the faulty section. The method of traveling waves with a single end is utilized to precisely fix the fault distance within the faulty segment when the type of traveling wave has been identified. The simulation results demonstrate that the method provides precise measurements and is effective for hybrid transmission lines with a range of fault distances and grounding resistances.

Challenges and solutions of Artificial Intelligence-based fault location methods in power system lines

<p class="Abstract">The accurate and efficient location of faults in power system lines is crucial for ensuring reliable and uninterrupted power supply. In recent years, Artificial Intelligence (AI) has been increasingly used in fault location methods, promising to improve the accuracy and efficiency of fault location. However, AI-based fault location methods also face challenges such as data quality, interpretability, and model robustness. <strong>Review method:</strong> This paper presents a review of the challenges and solutions of AI-based fault location methods in power system lines. The review is based on a comprehensive analysis of existing literature and research studies, focusing on the challenges associated with AI-based fault location methods and the solutions proposed to address these challenges. <strong>Content:</strong> The paper discusses the challenges associated with AI-based fault location methods in power system lines, including data quality, interpretability, and model robustness. The review presents several solutions to address these challenges, including data preprocessing techniques to improve data quality, explainable AI methods to enhance interpretability, and robustness validation techniques to improve model robustness. The accurate and efficient location of faults in power system lines is crucial for ensuring reliable and uninterrupted power supply. AI-based fault location methods have the potential to improve the accuracy and efficiency of fault location. However, these methods also face challenges such as data quality, interpretability, and model robustness. Addressing these challenges through techniques such as data preprocessing, explainable AI, and robustness validation can help to improve the accuracy and reliability of AI-based fault location methods.</p>

Accuracy analysis using the EMD and VMD for two-terminal transmission line fault location based on traveling wave theory

This paper presents an accuracy analysis from the application of techniques for the extraction of characteristic data from current and voltage signals in transmission line fault location based on traveling wave theory. The techniques used were the empirical mode decomposition and the variational mode decomposition associated with Teager energy operator, which helps to identify the instantaneous energy of the first intrinsic mode function. The simulation of the power system was carried out using the MATLAB/Simulink® software, for a test system consisting of a 200 km long transmission line between two Thevenin equivalents. Data is obtained from both terminals. The fault resistance was fixed at 100 Ω, but the incidence angle, the sampling rate and the fault location were varied for all fault types. The numerical and graphical results proved that the techniques can extract the characteristic data of the current and voltage signals and estimate the fault distance to the terminal with high accuracy, depending only on the sampling rate adopted.

Neural Network based Transmission Line Fault Classifier and Locator using Sequence Values

Faults can easily occur at transmission line because the line is exposed to the environment. The fast fault location after a fault occurrence will minimize the time to repair the faulty part thus reduce the stress of power system due to long outage time. This paper demonstrates the development of fault classifier and fault locator using neural network. The positive, negative and zero sequence values of three-phase voltage and current during fault time were used as the inputs to train the neural networks. Various fault conditions which have different fault types, fault resistance values and fault locations have been simulated to generate the training data for both neural network based fault classifier and fault locator. From the results, the fault classifier and locator successfully classified and located all the simulated fault conditions. One of the advantages of using sequence values as the inputs for the neural networks is only one fault type need to be simulated and used as the training data for single line-to-ground fault (RG, YG and BG), line-to-line fault (RY, YB, BR) and double line-to-ground fault (RYG, YBG, BRG).

Adaptive Fault Locator for High-Voltage Transmission Lines Based on the Estimation of Power System Model Parameters

Abstract This paper presents an adaptive transmission line fault location method, which incorporates fault location devices at both line ends and utilises data import from a supervisory control and data acquisition system without strictly requiring data synchronisation. The developed method aims at achieving a higher degree of robustness, adaptiveness and accuracy. The adaptiveness is achieved by dynamic updating of mathematical models used on the basis of network-wide information, such as data on the state of circuit-breakers and apparent power at load and generation nodes. The robustness and accuracy are enhanced by incorporating two stages of identification of model parameters with the goal of reducing the decision variable space for the stage identifying fault parameters. Furthermore, in addition to utilisation of all measurements available at a particular substation, the developed method partially employs the measurements from the other end of the line by means of result cross-checks, but does not require a full data set, unlike deterministic-model-based methods. An optimisation-based approach, redundancy on the basis of extended measurement set, and cross-checks reduce the risk of fault location errors due to measurement errors or “voids” in the data available. Testing of the developed method demonstrates its accuracy and robustness in a wide range of pre-fault and fault regime scenarios, even when considering various pre-fault contingencies.