A Novel Traveling Wave Based Fault Location Algorithm for Teed-Circuits

Travelling wave fault location method has been used in power system, but its performance was affected by the frequency band limitation (<;100kHz) of traditional current transformer (CT). As a new kind of transducer, Rogowski coil based electronic current transformer (R-ECT) has wide frequency bandwidth (several Hz to 1MHz) and differential output characteristic. Hilbert-Huang transform (HHT) is an analysis method for irregular signal and it can identify the mutation point of differential travelling wave precisely. Based on the instantaneous amplitude of differential travelling wave's Hilbert-Huang transform, the paper constructs new wave front identification criteria used in fault location method. Considering different fault locations and fault types, a 500kV power system simulation is carried out in PSCAD and the results prove the high accuracy of proposed fault location principle. To verify the fault location simulation, an experiment system is established using travelling wave signal generator, data acquisition device and Rogowski coil. The experiment result confirms the feasibility of fault location method.

Quick and accurate location of faults is the basic task of fault location as well as an important research subject in power system relay protection. The traveling wave principles of double terminal fault locating method is described, and the main causes of the generation of errors in actual application are investigated. A novel double terminal fault locating method is presented based on the existing fault locating method. The arriving intervals of the travelling wave from the fault location, reflected wave from fault location and reflected wave from opposite bus are recorded. The new fault location method is applied in Zhuzhou power grid, and the results show that it has high location accuracy.

In order to reduce the dispersion characteristics of Single-phase ground fault traveling wave propagation to the first detection of traveling wave and velocity measurements in the medium voltage distribution network, improve the accuracy of traveling wave fault location, the traveling wave fault location method be proposed based on wavelet packet energy spectrum of the medium voltage distribution network. This method will be combined wavelet packet and spectrum analysis of Fourier transform, extraction band signal to extract fault of the relative concentration of energy to location the traveling wave fault. The time of the first wave will be calculated by the band corresponding scale wavelet packet energy spectrum extracted wave characteristic points. A large number of PSCAD/EMTDC models of the IEEE34 Node simulation model results show that: The traveling wave distribution system fault location method can effectively extract the features of the signal wave power, reduce the dispersion characteristics and wave propagation changes of line length. Location error is less than 300m

In the single-end traveling wave (TW) fault location methods, for determining TW front, the arrival time of which is determined by the place of the short circuit (SC) on the power line, electrical network of fault regime models are constructed. From the electrical network of fault regime models, only one is selected that allows, by the first TW front magnitude and its arrival time, to obtain estimations of the TWs fronts magnitudes and their arrival times which are closest to the corresponding quantities determined from locator measurements. Based on the selected electrical network of fault regime model the used TWs are identified and the fault place is determined. Known implementations of the single-end traveling wave fault location method use simplified electrical network of fault regime model: the influence of the fault type and its resistance, as well as the parameters of the electrical network elements, are not taken into account on the TWs fronts magnitude. These disadvantages can cause both an increased error in determining the fault location and even failure in the operation of the locator. In this article, the theory of constructing electrical network of fault regime model is presented: the influence of fault location and its type on the TWs fronts magnitude are considered. Particular attention is paid to the study of the issue of the TWs generation as a result of the cross-transmission effect. It is shown that in order to correctly determine the used TW front in the electrical network of fault regime model, in addition to the power line length and its characteristic impedance, it is necessary to take into account the short circuit type and its resistance, and the possible TWs generation in one mode under the influence of TWs in another one.

A multi-terminal traveling wave fault location method for active distribution network based on residual clustering

In railway automatic blocking/power continuous line, quickly determining the fault location after a ground fault occurs is of great significance to its safe operation. This paper studies a traveling wave fault location method based on noncontact distributed measurement of railway power continuous line. Use the distributed traveling wave acquisition terminal to receive the space electromagnetic wave signal of the railway power continuous line, and use the noncontact method to collect the traveling wave to obtain the three-phase composite traveling wave quantity of voltage and current. The collection terminal does not need to be installed on the transmission line, avoiding power outages caused by equipment installation and maintenance. Through the wave speed normalization method based on the equivalent line, the influence of the wave speed discontinuity of the mixed line of overhead lines and cables is eliminated. The wavelet transform modulus maximum method is used to determine the arrival time of the traveling wave to determine the fault location. Simulation and practical application test verify the effectiveness and practical value of the method.

Single-ended travelling wave fault location methods have the advantages of avoiding the complexities and costs of the remote-end synchronisation. However, the determination of the arrival time of the travelling wave and its corresponding propagation velocity is a difficult problem which is apt to limit the algorithms' accuracy. A novel single-ended fault location method based on travelling wave time–frequency characteristics for transmission lines is proposed in this study. The time–frequency characteristics of the travelling wave signal are described by the Lipschitz exponent. The Lipschitz exponent of the second transient wave-front signal is estimated by using the wavelet transform and the least square method. Afterwards the frequency component of the travelling wave, which is best for fault location, and its corresponding arrival time and propagation velocity can be determined. Then the proposed method calculates the fault distance precisely based on the determined time and velocity. Extensive simulations have been carried out in PSCAD/EMTDC, and the results demonstrate that the accuracy of the proposed fault location method is significantly improved comparing with that of the traditional travelling wave fault location method. The proposed method is insensitive to different fault conditions, and it adapts to both transposed and untransposed lines well.

Fault location in electrical energy distribution networks is an important task, as faults in distribution grids are among the main causes of electricity supply disruption. Fault location in the distribution systems, however, is a challenging task because of the topology of the distribution networks, as well as the main and side branches. Therefore, it is necessary to address these challenges through an intelligent approach to fault location. In this paper, fault location in electric energy distribution networks is addressed considering the changes in fault distance and fault resistance in the presence of different fault types. A new method for fault location is developed for conditions where the minimum information is available and only information at the beginning of the feeder is used. This facilitates wide adoption of the technique as it does not require significant investments in instrumentation and measurement. The proposed intelligent method is based on the impedance and transient state estimation. This technique employs a specific impedance analysis for determining possible fault locations considering the unbalanced performance of distribution systems, distances, and different fault resistances. To determine the real faulty section, real fault frequency component analysis and the simulated faults at possible fault locations are used. At this stage of the process, it is possible to eliminate multiple estimations with the help of comparison and identification of the similarities. Therefore, a real faulty section is determined. It is observed that some conditions of electric energy distribution networks affect the accuracy and performance of the proposed method significantly; thus, a detailed investigation is conducted to neutralize these conditions. Simulation results and calculations based on MATLAB along with a practical test of the proposed method in power network simulator confirm a satisfactory performance.

Distribution network fault location technology is currently mainly concentrated in sub section positioning and based on the offline signal injection method precise positioning stage, this paper proposes a new online traveling wave fault location method based on the correlation matrix, the method based on line distributed terminal high sampling data waves, with terminal data construction association matrix, through the correlation matrix of the symmetry and correlation determine fault section and identify abnormal data set, on the basis of further accurate fault location, so as to improve the reliability and accuracy of fault location in distribution network.

The use of modern methods for determining the fault location (FL) on overhead power lines (OHРLs), which have high accuracy and high speed, allows you to quickly find and repair damage. Various physical principles and algorithms for calculating the distance to the site of damage are used in FL OHРLs devices from different manufacturers. Some of algorithms for FL on OHРLs use the emergency mode parameters (EMP), other algorithms use measurement results based on wave methods. Many random factors that determine the magnitude of the error in calculating the distance to the FL affect the operation of FL devices by EMP. Methods based on deterministic procedures used in well-known FL devices for OHРLs do not take into account the influence of random factors, which significantly increases the time to search for the fault. The authors have developed a new method of FL on OHРLs using multi-hypothesis sequential analysis and the Armitage algorithm. The task of recognizing a faulted section of an OHРL is formulated as a statistical problem. To do this, the inspection area of the OHРL is divided into many sections, followed by the implementation of the procedure for FL. The developed method makes it possible to adapt the distortions of currents and voltages on the emergency mode oscillograms to the conditions for estimating their parameters. The results of the calculations proved that the implementation of the developed method has practically no effect on the speed of the FL algorithm for the OHРL by EMP. This ensures the uniqueness of determining the faulted section of the OHРL under the influence of random factors, which leads to a significant reduction in the inspection area of the OHРL. The application of the developed method in FL devices for OHРLs will ensure the required reliability of power supply to consumers and reduce losses from power outages by minimizing the time to search for a fault.

In electrical power systems, transmission line fault location has been a widely investigated topic. Among the existing fault location methods, traveling wave (TW)-based solutions have shown to be the most accurate alternative. As a result, the development and evaluation of single-ended TW fault location (SETWFL) methods have attracted the interest of utilities, mainly due to its reduced cost and ease of application. In this context, although a commercially available SETWFL solution based on the recognition of fault-induced transient patterns has shown to be promising, further studies on how the sensitivity of TW detectors influence the method's performance are still required. Thus, this paper evaluates the relay transient detection sensitivity when applying a commercially available SETWFL function.

Fault location based on variable mode decomposition and kurtosis calibration in distribution networks

In software engineering, empirical studies on automated fault localization (FL) methods mainly focus on general software, and substantial progress has been made. However, the applicability and efficacy of these methods in specialized, domain-specific software like industrial software remains under-explored. Such specialized software is usually characterized by complex inputs and iterative computing paradigms, which could significantly influence the effectiveness of existing FL methods. To address this gap, this study takes a typical categorical of industrial software (i.e., computer-aided engineering (CAE) projects) as a case study, to investigate the feasibility and effectiveness of state-of-the-art FL methods within CAE projects. Through the reproduction of 76 real-world bugs from three widely used CAE projects (i.e., FDS, deal.II, and MFEM), we find that even the most precise FL methods require developers to examine on average 467.18 statements before finding bugs and can take 208.13 hours to execute. The complex inputs and long-term computation characteristics of CAE projects further increase the difficulty of FL. Moreover, FL on CAE also faces challenges, such as insufficient differentiation of coverage information and missing CAE-specific FL features. Based on our findings, we improve FL on CAE projects by proposing a set of CAE main module-based features, which improve the best-performed FL method in this study (i.e., DeepFL) by 35.93% and 45%, in terms of MAR and MFR , respectively.

Existing traveling wave fault location systems employ the double-end method and do not make use of data from neighboring substations. If one of the data acquisition equipments at either end of a line fails to capture the fault transient, no fault location result can be obtained. A wide-area traveling wave fault location (WA-TWFL) system, which makes use of traveling wave data from various substations across the monitored network, is introduced. The WA-TWFL algorithm first identifies the area of the monitored network in which the source of the disturbance originated by analyzing the traveling wave propagation times using the extended double end method. Then, the faulty line, and the distance to fault, are determined using selected records from the identified area of the disturbance. This approach provides fault location reliability than the traditional TWFL algorithm. Traveling wave data models based on IEC61850, which supports open communication and interoperability between data acquisition equipment and the master station of the WA-TWFL, are also presented in this paper. The feasibility of the proposed algorithm is proved using traveling wave data from an actual fault on a power grid in China.

Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. However, the existing deep learning-based FL methods ignore the deep semantic features or only consider simple code representations. And for APR tasks, existing template-based APR methods are weak in selecting the correct fix templates for more effective program repair, which are also not able to synthesize patches via the embedded end-to-end code modification knowledge obtained by training models on large-scale bug-fix code pairs. Moreover, in most of FL and APR methods, the model designs and training phases are performed separately, leading to ineffective sharing of updated parameters and extracted knowledge during the training process. This limitation hinders the further improvement in the performance of FL and APR tasks. To solve the above problems, we propose a novel approach called MTL-TRANSFER, which leverages a multi-task learning strategy to extract deep semantic features and transferred knowledge from different perspectives. First, we construct a large-scale open-source bug datasets and implement 11 multi-task learning models for bug detection and patch generation sub-tasks on 11 commonly used bug types, as well as one multi-classifier to learn the relevant semantics for the subsequent fix template selection task. Second, an MLP-based ranking model is leveraged to fuse spectrum-based, mutation-based and semantic-based features to generate a sorted list of suspicious statements. Third, we combine the patches generated by the neural patch generation sub-task from the multi-task learning strategy with the optimized fix template selecting order gained from the multi-classifier mentioned above. Finally, the more accurate FL results, the optimized fix template selecting order, and the expanded patch candidates are combined together to further enhance the overall performance of APR tasks. Our extensive experiments on widely-used benchmark Defects4J show that MTL-TRANSFER outperforms all baselines in FL and APR tasks, proving the effectiveness of our approach. Compared with our previously proposed FL method TRANSFER-FL (which is also the state-of-the-art statement-level FL method), MTL-TRANSFER increases the faults hit by 8/11/12 on Top-1/3/5 metrics (92/159/183 in total). And on APR tasks, the number of successfully repaired bugs of MTL-TRANSFER under the perfect localization setting reaches 75, which is 8 more than our previous APR method TRANSFER-PR. Furthermore, another experiment to simulate the actual repair scenarios shows that MTL-TRANSFER can successfully repair 15 and 9 more bugs (56 in total) compared with TBar and TRANSFER, which demonstrates the effectiveness of the combination of our optimized FL and APR components.