Transmission Line Fault Location Research Articles

A fault location method for DC transmission line based on the distributed parameter model considering line parameter uncertainty

Addressing the impact of whether line parameters can be accurately obtained and the errors due to environmental influences on the accuracy of fault location for DC transmission line, this paper proposes a fault location method based on the distributed parameter model that accounts for uncertainty in line parameter. To address the challenge of accurately obtaining DC transmission line parameters, a high-order derivative-based parameter estimation method is introduced to determine unknown equivalent parameters of the DC transmission line. Additionally, an optimization algorithm is incorporated to dynamically correct parameter errors, thereby enhancing parameter stability and constructing an accurate line model. Based on this, theoretical derivations confirm the applicability of the Tellegen's quasi-power theorem in the normal operation and internal fault equivalent models of DC transmission line. A fault location function is established based on the relationship between the line's Tellegen quasi-power characteristic and fault distance. The proposed method's effectiveness and accuracy are validated using a DC system model built on the PSCAD/EMTDC simulation platform. Simulation results indicate that the proposed method can locate faults under different fault conditions.

Long-distance transmission conductor condition sensing based on distributed fiber optic sensing technology

Abstract The existing long-distance transmission line perception mainly focuses on the measurement and analysis of electrical parameters. When the line is subject to wind vibration, icing or galloping, the changes of electrical parameters are not obvious and difficult to capture, resulting in poor performance of long-distance transmission line fault state perception. In this regard, the long-distance transmission line condition sensing based on distributed optical fiber sensing technology is studied. This method designs corresponding sensing methods for four working conditions: using the back Brillouin scattering sensor and phase sensitive Rayleigh scattering sensor in the optical fiber sensing technology to form a multi parameter distributed optical fiber sensing device, which is used to sense the surface temperature, vibration, strain and other data of the long-distance transmission line; By analyzing the linear relationship between fiber Brillouin frequency shift and Brillouin power and fiber strain and temperature, the sensing results of icing thickness of long-distance transmission lines are obtained; By calculating the amplitude and phase information of the detection signal, the sensing results of long-distance transmission line vibration are obtained; By calculating the time difference of polarization mutation signal at both ends of long-distance transmission line, combined with the propagation speed of optical signal and the length of long-distance transmission line, the perception result of lightning fault location of long-distance transmission line can be obtained. The experimental results show that this method can more accurately collect the real-time operation data of long-distance transmission lines, and can effectively perceive the galloping, wind vibration and lightning fault location of long-distance transmission lines.

INTELLIGENT FAULT DETECTION AND LOCATION IN ELECTRICAL HIGH-VOLTAGE TRANSMISSION LINES

Fault location in transmission lines is critical to ensure power systems' reliable and efficient operation. Accurate fault detection and localization are essential to minimize downtime, prevent cascading failures, and maintain the overall stability of the electrical grid. Over the years, various fault location methods have been proposed, ranging from traditional model-based approaches to more sophisticated artificial intelligence techniques. This research presents two fault location methodologies: the Atom search optimization metaheuristic approach (ASO) and machine learning (ML) with cubic spline models. We evaluate the performance of both approaches by considering different fault types, fault distances, and fault resistance. We analyze accuracy and computational efficiency. The findings reveal that the Metaheuristic Approach demonstrates robustness in fault detection and localization under diverse conditions but may suffer from higher computational overhead. In contrast, the hybridization of machine learning and metaheuristic exhibits promising potential in achieving real-time fault localization with improved accuracy.

Determining Fault Location in Transmission Lines Using Differential Equation Algorithms

A transmission line, in the context of electrical engineering and power systems, is a high-voltage or high-tension line that is used to transmit electrical power over long distances from power generation sources (such as power plants) to substations, where the electricity is then distributed to homes, businesses, and industrial facilities. Transmission lines are the backbone of the electrical grid, serving as the highways for electricity delivery. Their efficient and reliable operation is essential for ensuring that electricity is generated, transmitted, and distributed effectively, meeting the needs of consumers while maintaining grid stability and resilience. Transmission line protection refers to a set of techniques and devices used in electrical power systems to detect and respond to faults or abnormal conditions that can occur in high-voltage transmission lines. These lines are a critical part of the electrical grid and are responsible for transporting electricity over long distances from power generation plants to distribution substations. This paper proposes a different differential equation algorithm to locate faulty section on transmission lines. As seen from performance curves, the proposed protection algorithm is able to distinguish normal and faulty conditions.

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

Artificial Hummingbird Algorithm-based fault location optimization for transmission line

Transmission is an important aspect regarding an effective designing of electric supply system. Ensuring reliable and fault-free transmission from the source for effective distribution to the end consumers is very much desirable. In this respect, fast and accurate fault detection, particularly in the overhead transmission lines, is very pertinent. Various algorithms and novel approaches have been formulated by various researchers aligned to this challenge. In this context, a new algorithm influenced by the biotic procedure of flight skills of hummingbird seems to be one of the best algorithms to address the cited problem. This paper focuses on the formulation of this Artificial Hummingbird Algorithm (AHA) and its high accuracy in ameliorating the fault location in transmission line. The most common flight skills being used in the algorithm are foraging schemes, which includes axial, diagonal, and omnidirectional flights. The proposed AHA has been tested using the Simulink prototype in MATLAB for an overhead transmission line having a length of 300 km and system voltage of 400 kV at suitable lengths. Specimen signal of voltages and currents waveforms has been taken at duo ends of the overhead transmission line. The results of the proposed algorithm have been compared with the results obtained from previous studies, and it has been observed that this algorithm yields better results for various kinds of asymmetrical and symmetrical faults.

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.

Fault location of transmission lines by wavelet packet decomposition based on SSSC and EMD

Fault location of transmission lines by wavelet packet decomposition based on SSSC and EMD

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.

Enhancing Fault Identification, Classification and Location Accuracy in Transmission Lines

This research paper presents a proposed system for fault identification, classification and location in transmission lines using a Support Vector Machine (SVM)-based technique in conjunction with a Positive Sequence Analyzer. The objective is to develop an accurate and reliable method for identifying, classifying and locating different fault types in transmission lines. The proposed system leverages the capabilities of SVMs in handling high-dimensional feature spaces and the fault signature extraction capabilities of the Positive Sequence Analyzer. Experimental evaluations are conducted to assess the performance and effectiveness of the proposed system, comparing it with existing fault identification and classification methods. The results demonstrate the superior performance and robustness of the SVM-based technique utilizing the Positive Sequence Analyzer, providing a valuable contribution to fault management and system reliability in transmission line networks.

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.

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.

Multi-Terminal Fault Locator for Homogeneous Transmission Lines Using Extended Proportions

Multi-Terminal Fault Locator for Homogeneous Transmission Lines Using Extended Proportions

VSC-HVDC transmission line fault location based on transient characteristics

VSC-HVDC transmission line fault location based on transient characteristics

Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines

Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines

Fault location of high voltage overhead transmission line based on ACO-ENN algorithm.

Fault location of high voltage overhead transmission line based on ACO-ENN algorithm.

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.