Fault location on overhead power lines using multi-hypothesis sequential analysis and the Armitage algorithm

DC fault current is contributed by various distributed energy resources in DC distribution systems. Most DC distribution systems have low line impedance. The fault current increases fast because of the low impedance. Some converters in DC distribution systems include Fault Current Limiting (FCL) function. The controlled fault currents at different locations are very close. Thus, it is important to design a reliable and fast fault detection and location method for DC distribution systems. This paper proposes a novel local measurement-based fault location algorithm for DC distribution systems. The proposed fault location algorithm can estimate the equivalent inductance from a protective device to a fault in less than one millisecond. The performance of the developed protection algorithm was validated in both numerical simulation and hardware testing.

Based on the traveling wave fault location principle, the traveling wave fault location method of Teed circuit is studied, and the idea of comprehensively using the three-terminal measurement data of Teed-circuit for fault location is proposed, and a new criterion for fault branch identification and a new method for fault location are proposed accordingly. The branch identification criterion considers the influence of the actual fault location error factors on the branch misjudgment to ensure the validity of the branch identification; and the proposed fault location method makes full use of the three-terminal measurement data of Teed-circuit. Meanwhile, the influence caused by the uncertainty of wave velocity on the fault location result is eliminated in the fault location expression, and the fault location error caused by the line sags is eliminated to a certain extent. The ATP/EMTP simulation results show that the method proposed in this paper can accurately identify fault branches and locate fault points on Teed-circuit.

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.

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.

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.

A fault detection and location algorithm on three-terminal transmission line is proposed. Vast literature on fault location shows that to simplify the analysis, line charging currents are often ignored. To improve the accuracy of fault location, synchronized current and voltage measurements from three ends of the three-terminal line are utilized to detect and locate distance to fault while considering the line charging currents. The method first detects the zone at which the fault has occurred and then locates it from the respective line ends. Since three-terminal lines provide solutions for connecting existing system to the grid, facilitating bulk transfer of power, thus the fault detection and location on the same is important to maintain reliability and security of the system. Performance of the proposed algorithm is tested for various fault types, fault locations, fault resistances and fault inception angles. The 200-kV system is simulated in ATP/EMTP; however, the algorithm is developed in MATLAB. The extensive simulation studies demonstrate accuracy of the proposed scheme.

Identification of fault locations (FL) on overhead power lines (OHPL) in the shortest possible time allows to reduce the time for shutting down OHPL in case of damage. This helps to improve the reliability of power systems. FL devices on OHPL according to the emergency mode parameters (EMP) are widely used, as they have a lower cost. However, they have a larger error than FL devices that record traveling wave processes. Most of the well-known algorithms for FL on OHPL by EMP assume a uniform distribution of resistivity along the OHPL. In real conditions, this is not the case. Application of these algorithms in FL devices on OHPL with inhomogeneities leads to significant errors in calculating the distance to the fault location. The authors proposed the use of search algorithms for u-nconstrained one-dimensional optimization to increase the speed of implementation of iterative procedures in FL devices on OHPL by EMP. The authors have developed recommendations for choosing optimization criteria, as well as options for implementing computational procedures. Using the example of a two-sided FL on OHPL, it is shown that the use of search algorithms can significantly (from tens to hundreds of times) reduce the number of steps of the computational iterative procedure. The implementation of search algorithms is possible in the software of typical relay protection and automation terminals, without their hardware upgrade.

A new method of fault location of distribution network based on the tree structure diagram and improved Dempster-Shafer Theory of evidence is proposed to solve the problem of misjudgment caused by abnormal fault information in distribution network. Firstly, this paper presents a new search method based on tree structure diagram. Through the tree structure diagram, we can ensure the initial positioning result respectively based on three different alarm information. The three alarm information include fault indicator information, distribution transformer information and trouble calls. And then, through using the improved Dempster-Shafer Theory of evidence, we can integrate the initial positioning results to get the final positioning result. Finally, a concrete example is given to demonstrate the effectiveness and feasibility of this method, which indicates that the method can solve the problem when misinformation or information happens. The outstanding advantage of this method is that the multi-source information is used to locate the fault, and the misjudgment due to the abnormality of the single source information can be avoided.

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.

Today, there is no doubt that optical networks are the solution to the explosion of Internet traffic that two decades ago we only dreamed about. They offer high capacity with the use of Wavelength Division Multiplexing (WDM) techniques among others. However, this increase of available capacity can be betrayed by the high quantity of information that can be lost when a failure occurs because not only one, but several channels will then be interrupted. Efficient fault detection and location mechanisms are therefore needed. Our challenge is to identify and locate failures (single or multiple) at the physical layer of an optical network in the presence of some lost and/or false alarms. After briefly introducing optical networks and the multiplexing techniques that can be used, we study the most common components and their most usual failures. We propose a classification of all the network components based on their behaviour when failures occur. This classification gives an abstract model of the optical network, which is appropriate for developing algorithms to locate faulty elements. Two algorithms that solve the fault location problem are proposed. Both algorithms cope with existence of false and missing alarms when locating single and multiple failures. The problem of locating multiple failures already in the absence of false or missing alarms, has been shown to be NP-complete. The first algorithm, which is called Alarm Filtering Algorithm (AFA) is based on the combination of two approaches: forward and backward. The forward approach returns for each network element, their domain, which is the set of network elements that will send an alarm when the considered element fails. The backward approach returns the set of elements that are directly related to the received alarms. In this approach, the alarms that are considered to provide redundant information, are discarded. The combination of the results given by both approaches allows the location of multiple failures, given an allowed number of false and missing alarms. However, this algorithm does not minimize the complexity when new alarms are received. Hence, a second algorithm, which is called Fault Location Algorithm (FLA), is proposed. The FLA concentrates the complexity in ,a pre-computation phase, so that when new alarms are received, the result of the algorithm is rapidly displayed. The FLA algorithm is based on the construction of a binary tree that realizes a non linear error correcting code. The FLA has also been extended to locate soft failures in addition to hard failures. Hard failures are unexpected failures, whereas soft failures are progressive failures due to equipment aging, misalignments or external factors such as temperature or pressure. Both algorithms are compared on some simulated networks using different network topologies and failure cases. The comparison has also be done on the basis of their worst case complexity. Some conclusions indication with which settings each algorithm perform the best, were obtained.

Test flakiness (non-deterministic behavior of test cases) is an increasingly serious concern in industrial practice. However, there are relatively little research results available that systematically address the analysis and mitigation of this phenomena. The dominant approach to handle flaky tests is still detecting and removing them from automated test executions. However, some reports showed that the amount of flaky test is in many cases so high that we should rather start working on approaches that operate in the presence of flaky tests. In this work, we investigate how test flakiness affects the effectiveness of Spectrum Based Fault Localization (SBFL), a popular class of software Fault Localization (FL), which heavily relies on test case execution outcomes. We performed a simulation based experiment to find out what is the relationship between the level of test flakiness and fault localization effectiveness. Our results could help the users of automated FL methods to understand the implications of flaky tests in this area and to design novel FL algorithms that take into account test flakiness.

The MV networks are not construct as transposed that means they are non-symmetrical. This factor causes the unbalancing of the phase voltages and the zero component of voltage appears there. The distribution networks with overhead lines are operated as compensated with the arc-suppression coil connected to node of transformer. The operation with the arc-suppression coil out of tune is accompanied by higher values of the fault currents during the ground faults and the natural frequency of the course of recovery voltage in the network after switching off or interrupting the ground fault. These disadvantages can only be removed by efficient elimination of the phase asymmetry. This paper describes new method of elimination phase capacitance asymmetry by ancillary suppression coils. This method has favorable effects on the network operation during the ground fault and do not reduce the protecting elements sensitivity to high-ohm ground faults. By improving the accuracy of the arc-suppression coil tuning the safety of the compensated networks operation can be enhanced. The new method of elimination of the phase asymmetry opens the secondary advantage like new method of fault location. This location method starts from idea of fault location using inter-harmonic signal [1,3,7] or remote control signal [6] and is based on injection of higher inter- harmonic frequency signal to the secondary windings of ancillary suppression coil which is connected to affected phase. With Ohm law application it is possible to determine the impedance of fault-loop and from this the distance of fault too.

Fault location for power grid based on transient travelling wave data fusion via asynchronous voltage measurements

Penetration of renewable energy sources (RESs) in power systems is increased considerably and fault location algorithms face considerable problems with the presence of RESs. Also, the third terminal of three-terminal lines is expanded as a cost-effective solution to energize the remote areas of medium voltage (MV) systems, while its data may not be measured and easily available. In addition, in cases where this data is obtainable, due to the excessive harmonics of the inverter-based resources (IBRs) in that terminal, the measured data includes high error. Therefore, providing a method for fault location in three-terminal lines in the presence of RESs is challenging. This paper proposes an accurate method to identify the fault location on these lines incorporating RESs without requiring the third terminal data. Due to current transformers saturation and transient response of capacitive voltage transformers, the fault location based on the faulted phase data contains significant errors. The proposed method combines both the faulty and healthy phases data to improve the accuracy of fault location. Its performance is evaluated on a MV system for different scenarios including various fault resistances, locations and types, and third terminal integration of RESs. The obtained results confirm superiority of the proposed method.