Fault Localization Techniques Research Articles

Numerical investigation of bandgap characteristics in integrated metallic metafilters for nonlinear guided wave applications

Guided waves propagating in nonlinear media, featuring second harmonic generation, represent a promising avenue for early-stage damage detection due to their high sensitivity and long-range propagation capabilities. However, nonlinear ultrasonic measurements are hindered by nonlinearities induced by the experimental system, necessitating careful calibrations that have restricted their application to laboratory settings. While several phononic crystal and metamaterial designs have been devised to enhance nonlinear-based ultrasonic testing, most are tailored for suppressing second harmonics within a frequency range of 100–300 kHz, primarily utilizing low-frequency excitation. In this paper, we propose a metallic ring-shaped metafilter designed to explore high-order bandgaps. To fully understand the bandgap characteristics, we begin by analyzing mode shapes, providing insights into the underlying wave mechanics. The efficacy of the designed filter is subsequently assessed through 3D time step elastodynamic simulations. In addition, this study underscores the significance of parameters such as the number of rings employed in the filter, signal duration, and bandgap width in optimizing its performance. Furthermore, the observed mode conversion phenomena from S0 to A0 guided wave modes underscore the filter’s capacity to influence guided wave propagation. The defect localization technique, based on the time difference of arrival of second-order wave modes, accurately predicts the defect location with an error margin of less than 0.2%. The present investigation showcases advancements in the sensitivity of nonlinear-based guided wave testing for characterizing microstructural changes, promising substantial potential for detecting incipient damage in practical structural health monitoring applications.

An Enhanced Fault Localization Technique for Distribution Networks Utilizing Cost-Sensitive Graph Neural Networks

Accurate and timely fault diagnosis is of great significance for the stable operation of a distribution network. Traditional artificial intelligence-based localization methods rely heavily on large-scale labeled datasets, making them prone to being affected by distributed generators. To address this issue, this study proposes a fault location method for distribution systems using a cost-sensitive graph attention network model. In this approach, the physical structure of the distribution network is considered a crucial constraint for model training, thereby enhancing its information perception capabilities. Specifically, the electrical nodes and lines of the distribution network are mapped to the vertices and edges within the graph attention network, with the attention weights determined by the correlation of adjacent node fault characteristics, thus improving the sensitivity to grounding faults. Additionally, a cost-sensitive matrix is used to balance class distribution, enhancing the robustness and generalization ability of the model. Fault localization experiments were conducted on the IEEE-33 bus distribution system to validate the effectiveness of the proposed fault localization method. Factors such as data disturbance, varying fault grounding resistances, and distributed power supply access were employed to assess the model’s anti-interference performance. The experimental results demonstrate that the fault location method exhibits high positioning accuracy and excellent robustness.

A Ground Fault Location Technique for Wound Field Poles of Synchronous Machines Through Frequency Response Analysis

A Ground Fault Location Technique for Wound Field Poles of Synchronous Machines Through Frequency Response Analysis

GBSR: Graph-based suspiciousness refinement for improving fault localization

Fault Localization (FL) is an important and time-consuming phase of software debugging. The essence of FL lies in the process of calculating the suspiciousness of different program entities (e.g., statements) and generating a ranking list to guide developers in their code inspection. Nonetheless, a prevalent challenge within existing FL methodologies is the propensity for program entities with analogous execution information to receive a similar suspiciousness. This phenomenon can lead to confusion among developers, thereby reducing the effectiveness of debugging significantly. To alleviate this issue, we introduce fine-grained contextual information (such as partial code structural, coverage, and features from mutation analysis) to enrich the characteristics of program entities. Graphical structures are proposed to organize such information, where the passed and failed tests are constructed separately with the consideration of their differential impacts. In order to support the analysis of multidimensional features and the representation of large-scale programs, the PageRank algorithm is adopted to compute each program entity’s weight. Rather than altering the fundamental FL process, we leverage these computed weights to refine the suspiciousness produced by various FL techniques, thereby providing developers with a more precise and actionable ranking of potential fault locations. The proposed strategy Graph-Based Suspiciousness Refinement (GBSR) is evaluated on 243 real-world faulty programs from the Defects4J. The results demonstrate that GBSR can improve the accuracy of various FL techniques. Specifically, for the refinement with traditional SBFL and MBFL techniques, the number of faults localized by the first position of the ranking list (Top-1) is increased by 189% and 68%, respectively. Furthermore, GBSR can also boost the state-of-the-art learning-based FL technique Grace by achieving a 2.8% performance improvement in Top-1.

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Fault localization by abstract interpretation and its applications

Fault localization aims to automatically identify the cause of an error in a program by localizing the error to a relatively small part of the program. In this paper, we present a novel technique for automated fault localization via error invariants inferred by abstract interpretation. An error invariant for a location in an error program over-approximates the reachable states at the given location that may produce the error, if the execution of the program is continued from that location. Error invariants can be used for statement-wise semantic slicing of error programs and for obtaining concise error explanations. We use an iterative refinement sequence of backward–forward static analyses by abstract interpretation to compute error invariants, which are designed to explain why an error program violates a particular assertion.Furthermore, we present a practical application of the fault localization technique for automatic repair of programs. Given an erroneous program, we first use the fault localization to automatically identify statements relevant for the error, and then repeatedly mutate the expressions in those relevant statements until a correct program that satisfies all assertions is found. All other statements classified by the fault localization as irrelevant for the error are not mutated in the program repair process. This way, we significantly reduce the search space of mutated programs without losing any potentially correct program, and so locate a repaired program much faster than a program repair without fault localization.We have developed a prototype tool for automatic fault localization and repair of C programs. We demonstrate the effectiveness of our approach to localize errors in realistic C programs, and to subsequently repair them. Moreover, we show that our approach based on combining fault localization and code mutations is significantly faster that the previous program repair approach without fault localization.

A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization

Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce developer burden, many automated FL techniques have been proposed. However, prior work has noted that existing techniques fail to provide rationales for the suggested locations, hindering developer adoption of these techniques. With this in mind, we propose AutoFL, a Large Language Model (LLM)-based FL technique that generates an explanation of the bug along with a suggested fault location. AutoFL prompts an LLM to use function calls to navigate a repository, so that it can effectively localize faults over a large software repository and overcome the limit of the LLM context length. Extensive experiments on 798 real-world bugs in Java and Python reveal AutoFL improves method-level acc@1 by up to 233.3% over baselines. Furthermore, developers were interviewed on their impression of AutoFL-generated explanations, showing that developers generally liked the natural language explanations of AutoFL, and that they preferred reading a few, high-quality explanations instead of many.

Line to Ground Fault Detector in 400 KV Transmission Line by Using Intelligent System

The ability to locate and identify problems in power transmission lines is the most crucial component of power system engineering since it ensures the stable and efficient operation of electrical grids. to minimize downtime, prevent cascading failures, and maintain the power system's stability. problem localization helps identify the precise location of the problem and speeds up the process of restoring the power supply while minimizing the impact on consumers. This paper presents a measurement-based automatic fault localization and detection (ANFIS) technique for transmission lines. High-speed processing of real-time error localization and detection is made possible by the design and implementation of ANFIS. It is suggested that ANFIS for digital distance protection systems be able to discover issues in addition to detecting them. When a transmission experiences a three-phase malfunction Thus, the suggested method can precisely pinpoint the impacted stages. ANFIS has been trained and tested using many kinds of field data. The field data are collected by simulating faults in the Simulink Matlab model depicting the transmission line at various sites between Misan – Kut station 400 Kv along 200 Km using computer software based on Matlab. Phase current and voltage measurements are utilized as ANFIS inputs and are available at the busses. Regarding the kind and detection of defects, The output will demonstrate fault and location identification with an extremely low error percentage through simulated processes; the results also demonstrate that the approach's selectivity and speed are quite dependable and provide adequate performance for applications involving transmission and distribution monitoring; and security. The study emphasizes the criticality of promptly and precisely locating faults in power transmission lines to preserve the stability and security of the power system.

An empirical study of fault localization in Python programs

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several findings about programming languages like C/C++ and Java—the most common choices for fault localization research—carry over to other languages, whether the dynamic nature of Python and how the language is used in practice affect the capabilities of classic fault localization approaches remain open questions to investigate. This paper is the first multi-family large-scale empirical study of fault localization on real-world Python programs and faults. Using Zou et al.’s recent large-scale empirical study of fault localization in Java (Zou et al. 2021) as the basis of our study, we investigated the effectiveness (i.e., localization accuracy), efficiency (i.e., runtime performance), and other features (e.g., different entity granularities) of seven well-known fault-localization techniques in four families (spectrum-based, mutation-based, predicate switching, and stack-trace based) on 135 faults from 13 open-source Python projects from the BugsInPy curated collection (Widyasari et al. 2020). The results replicate for Python several results known about Java, and shed light on whether Python’s peculiarities affect the capabilities of fault localization. The replication package that accompanies this paper includes detailed data about our experiments, as well as the tool FauxPy that we implemented to conduct the study.

Multi‐objective optimization‐based and fault localization‐oriented test case generation for novice programs

SummaryOnline judgment (OJ) systems are capable of evaluating program results by automatically executing test cases, significantly improving the efficiency of traditional guidance approaches. Moreover, existing studies attempt to assist novices through automated fault localization techniques to provide feedback to novices, which can help them quickly find the location of faulty statements. Among them, spectrum‐based fault localization (SBFL) techniques have been widely used for their lightweight and efficiency, which only requires coverage information and test results of test cases to conduct fault localization. However, manually constructing high‐quality test cases for a large number of OJ questions is tough work to complete. To solve this problem, we propose the novice program‐oriented Multi‐Objective Optimization‐Based Fault Localization‐Oriented Test Case Generation (MFTCG) for automatically generating test inputs. Specifically, we use multi‐objective optimization algorithms to evolve the test case in terms of both fault localization and faulty code detection capability. We conduct experiments with 8911 programs from the well‐known public OJ platform AtCoder. The results show that our proposed approach MFTCG can achieve the best fault localization performance compared with existing automated test case generation approaches in most cases and can achieve the similar faulty code detection capability compared to manually designed test cases.

Boosting fault localization of statements by combining topic modeling and Ochiai

Context:Reducing the cost of maintenance tasks by fixing bugs automatically is the cornerstone of Automated Program Repair (APR). To do this, automated Fault Localization (FL) is essential. Two families of FL techniques are Spectrum-based Fault Localization (SBFL) and Information Retrieval Fault Localization (IRFL). In SBFL, the coverage information and execution results of test cases are utilized. Ochiai is one of the most effective and used SBFL strategies. In IRFL, the bug report information is utilized as well as the identifier names and comments in source code files. Latent Dirichlet Allocation (LDA) is a generative statistical model and one of the most popular topic modeling methods. However, LDA has been used at the method level of granularity as IRFL technique, whereas most existing APR tools are focused on the statement level. Objective:This paper presents our approach that combines topic modeling and Ochiai to boost FL at the statement level. Method:We evaluate our approach considering five different projects in Defects4J benchmark. We report the performance of our approach in terms of hit@k and MRR. To study the impact on the results, we compare our approach against five baselines: two SBFL approaches (Ochiai and Dstar), two IRFL approaches (LDA and Blues), and one hybrid approach (SBIR). In addition, we compare the number of bugs that are found by our approach with the baselines. Results:Our approach significantly outperforms the baselines in all metrics. Especially, when hit@1, hit@3 and hit@5 are compared. Also, our approach locates more bugs than Ochiai and Blues. Conclusion:The results of our approach indicate that the integration of topic modeling with Ochiai boosts FL. This uncovers the potential of topic modeling for FL at statement level, which is valuable for the APR community.

Application of a Centroid Frequency-Based Back Propagation Neural Network Fault Location Method for a Distribution Network Considering Renewable Energy Assessment

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m.

A double-ended third harmonic method for vegetation high impedance fault location in overhead distribution systems

Vegetation-related high-impedance faults (VHIFs) on overhead distribution systems can cause damage, maintenance issues, and even wildfires, impacting both utilities and ecosystems. Existing fault location techniques struggle to accurately locate VHIFs. In this paper, we propose a novel double-ended terminal location method based on the third harmonic component of the fault system. The proposed technique determines the distance of the VHIF by approximating the third harmonic voltage at the fault local, utilizing measurements from the remote terminal located at the far-end of the feeder. This method efficiently locates VHIFs with just two measurement terminals, cutting the need and cost of extensive sensor deployment. Moreover, the proposed method overcomes the limitations of traditional fault location techniques by using a third harmonic model of the faulted system, eliminating the need for complex mathematical models to approximate the fault behavior and voltage at the fault local. Simulation tests were conducted to validate the proposed method, demonstrating its effectiveness in accurately locating VHIFs. The technique achieved error rates below 1.6 %, making it a promising solution for real-world applications in power distribution systems. The ability to precisely locate VHIFs meets a proactive protection philosophy and contributes to improved power system protection and ensures uninterrupted power supply.

A novel fault location method for the active distribution network based on dynamic quantum genetic algorithm

The fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit.

A Fault Location Algorithm for Multi-Section Combined Transmission Lines Considering Unsynchronized Sampling

This paper introduces a fault location technique for multi-section combined transmission lines, utilizing unsynchronized fault record data. The approach determines the intersection points of the voltage magnitudes computed using the fault current and voltage records of each terminal and the constant line parameters of each section. Finally, the real fault location is identified based on the calculated inverse angle of equivalent impedance. To evaluate the performance of the proposed algorithm, a three-section combined transmission line is constructed using the PSCAD/EMTDC software version 5.0.0, and various fault scenarios are simulated and tested employing the proposed algorithm. The results indicate that the proposed algorithm is applicable to various types of faults in a multi-section combined transmission line; the estimations are accurate.

A deep semantics-aware data augmentation method for fault localization

Context:Fault localization (FL) techniques are employed to identify the relationship between program statements and failures by analyzing runtime information. They rely on the statistics of input data to explore the underlying correlation rooted in it. Consequently, the quality of input data is of utmost importance for FL. However, in practice, passing tests significantly outnumber failing tests regarding a fault. This leads to a class imbalance challenge that can adversely affect the effectiveness of FL. Objective:To tackle the issue of imbalanced data in fault localization, we propose PRAM: a deeP semantic-awaRe dAta augmentation Method to improve the effectiveness of FL methods. Method:PRAM utilizes program dependencies to enhance the semantic context, thus showing how a failure is caused. Then, PRAM employs mixup method to synthesize new failing test samples by merging two real failing test cases with a random ratio to balance the input data. Finally, PRAM feeds the balanced data consisting of synthesized failing test cases and original test cases to FL techniques. To evaluate the effectiveness of PRAM, we conducted large-scale experiments on 330 versions of nine large-sized real programs for six state-of-the-art FL methods, two data optimization methods and two data augmentation methods. Results:Our experimental results show that PRAM outperforms in most cases for Top-K metrics and reduces the number of checked statements from 40.38% to 80.04% compared with the original FL methods. Furthermore, PRAM reduces the checked statements from 16.92% to 56.98% for data optimization methods and from 12.48% to 26.82% for data augmentation methods. Conclusion:The experimental results show that PRAM is not only more effective than the original FL methods but also more effective than two representative data optimization methods and two data augmentation methods, which indicates that PRAM is a universal effective data augmentation method for various FL methods.

Electric Fault Location Methods Implemented on an Electric Distribution Network.

This paper compares two electric fault location techniques, [1]&[2]. Using prefault and fault data (voltages and currents) and a model of the network, the algorithms can give an approximation of the actual fault location. They were chosen because the final aim is to implement them on a real distribution network, where we have registered faults with the prefault and fault information, and where the parameters of the network are known. Both techniques were implemented on simple lines and tested through simulations. The results obtained, showed that Ratan Das algorithm was giving better approximations to the actual fault. On a second step the algorithms were implemented on a real distribution network. The results also showed that Ratan Das algorithm give better results and is less sensible to fault impedance, a parameter that in the majority of cases is unknown. An application with a graphical user interface has been created to execute the methods, but also to execute the necessary previous steps: obtain the phasors of the fault, either through a simulation or loading actual registered faults, graphical representation of waveforms and phasors... In conclusion, the Saha method is easy to implement but an evaluation of the fault resistance is needed, and this let the method to be more sensible at uncertainties.

A PLC communication characteristics‐based fault location method in medium voltage meshed distribution networks

AbstractA power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values.

Research on Fault Detection and Localization Techniques for Distribution Networks Based on Edge Clustering

Abstract With the intelligent transformation of the power grid, the number and type of various terminals, sensors, and new types of loads in the distribution network increase, and the huge amount of information and noise information accessing the power grid brings great challenges for fault detection and localization. In this paper, we propose to combine wavelet transform and LSTM unit to form a novel neural network basic unit TFM and examine the fault detection and localization capability of TFM by combining them with a typical example system, IEEE33. Spectral clustering and the K-Means algorithm are utilized to cluster the edge nodes, and the number of nodes in the three partitions after partition correction is 11, 10, and 12 nodes in order. The data shows that the TFM system performs the fault diagnosis task with better test accuracy than LSTM in every dimension, and its accuracy improvement for fault localization is the largest, at 5.39%. Introducing two reconfiguration scenarios for three edge node partitioning models and retrograde fault localization detection, compared with the no reconfiguration scenario, their localization accuracies all produce different degrees of decline, but always not less than 80%. The localization accuracy of the MⅡ model is still not less than 98% in the fault resistance range of 500~1000Ω, which proves that the TFM system can effectively extract high-resistance fault features.

Enhanced fault localization in multi-terminal transmission lines using novel machine learning

Accurate fault location on transmission lines is paramount for ensuring the reliable and efficient operation of the electricity grid, which underpins every aspect of modern society. Existing fault localization methods for transmission lines often face shortcomings, particularly in scenarios involving multi-terminal transmission lines, where complexities arise due to dispersed generations and intricate network configurations. Traditional approaches may struggle to provide accurate fault localization, impacting the reliability and efficiency of the electricity grid. Research provide a unique fault localization technique in this article depends on Phasor Measurement Units (PMUs) and Bidirectional Gradient Boost Random Forest (BDGB-RF) machine learning technique to address these challenges. The proposed method offers several advantages over traditional methods, including enhanced accuracy and efficiency. By leveraging PMU data and BDGB-RF, the method provides a two-phase fault localization strategy, incorporating fault line selection based on nodal current imbalance and subsequent fault distance determination. Simulation results demonstrate the effectiveness of the proposed approach, achieving up to 97% accuracy in the majority of studied scenarios, even under different tapping configurations. The adoption of this approach could significantly impact the practices of experts in the field, facilitating more reliable fault detection and localization in complex transmission line networks. This, in turn, can contribute to the resilience and stability of power systems, ultimately improving grid reliability and minimizing downtime.