Automatic Fault Location Research Articles

Automated fault location scheme for low voltage smart distribution systems

AbstractIntegrating the self‐healing capability realizes the automated protection of smart distribution systems. This article presents a fault location scheme for low voltage (LV) distribution systems based on the information collected from the smart meters. First, a fault condition is detected and classified by processing the current signal at the secondary side of the distribution transformer by an intelligent electronic device. Then, the faulty feeder and section are determined by calculating the fault‐imposed component of nodal voltages. The reliable performance of the proposed scheme is verified through several case studies using a real semi‐rural distribution system.

Method of automatic fault location in 6-35 kV electrical networks

The paper analyses the known means of fault location in electrical networks, identifies their disadvantages and proposes its own method of solving this problem. The model of electric network is built and investigated by MATLAB.

Automatic fault location method for power communication network based on a machine learning algorithm

The current conventional automatic fault location method of power communication network mainly realizes the identification of faulty branches by calculating node power parameters, which leads to poor location effects due to the lack of effective analysis of fault signal data. In this regard, the automatic fault location method of the store head communication network based on a machine learning algorithm is proposed. Firstly, the fault signal data are collected and pre-processed, specifically including data cleaning and data format conversion, and then the individual fault signal is used as the basis to differentiate the weight parameters of different abnormal degrees and combine with the state threshold to realize the judgment of the fault node interval location. In the experiments, the fault location performance of the proposed method is verified. The experimental results show that when the proposed method is used for fault location, the relative positioning error value of the method is small and has a high fault location accuracy.

Modeling and Simulation of Time Domain Reflectometry Signals on a Real Network for Use in Fault Classification and Location

Today, the classification and location of faults in electrical networks remains a topic of great interest. Faults are a major issue, mainly due to the time spent to detect, locate, and repair the cause of the fault. To reduce time and associated costs, automatic fault classification and location is gaining great interest. State-of-the-art techniques to classify and locate faults are mainly based on line-impedance measurements or the detection of the traveling wave produced by the event caused by the fault itself. In contrast, this paper describes the methodology for creating a database and a model for a complex distribution network. Both objectives are covered under the paradigm of the time-domain pulse reflectometry (TDR) principle. By using this technique, large distances can be monitored on a line with a single device. Thus, in this way a database is shared and created from the results of simulations of a real and complex distribution network modeled in the PSCAD™ software, which have been validated with measurements from an experimental test setup. Experimental validations have shown that the combination of the TDR technique with the modeling of a real network (including the real injector and the network coupling filter from the prototype) provides high-quality signals that are very similar and reliable to the real ones. In this sense, it is intended firstly that this model and its corresponding data will serve as a basis for further processing by any of the existing state-of-the-art techniques. And secondly, to become a valid alternative to the already well-known Test Feeders but adapted to work groups not used to the electrical world but to the environment of pure data processing.

Research on text fault recognition for on-board equipment of a C3 train control system based on an integrated XGBoost algorithm

Abstract The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train. A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost (eXtreme Gradient Boosting) algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly. The XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms. To begin, the text features were extracted using the improved TF-IDF (Term Frequency–Inverse Document Frequency) approach, and 24 fault feature words were chosen and converted into weight word vectors. Secondly, considering the imbalanced fault categories in the data set, the ADASYN (Adaptive Synthetic sampling) adaptive synthetically oversampling technique was used to synthesize a few category fault samples. Finally, the data samples were split into training and test sets based on the fault text data of CTCS-3 train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel. The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search. Compared with other methods, the evaluation index of the XGBoost model was significantly improved. The diagnostic accuracy reached 95.43%, which verifies the effectiveness of the method in text fault diagnosis.

Two novel current-based methods for locating earth faults in unearthed ring operating MV networks

Automated fault location plays a key role in improving the reliability of modern power grids—it helps to reduce the power outage time and the number of affected customers. Earth faults in isolated systems are among the fault types that are difficult to detect and locate with a reasonable accuracy, and there has therefore not been a well-established method for detecting and locating such faults in isolated systems. This paper presents two novel current-based methods for locating earth faults in unearthed closed-ring MV networks. The methods are based on the utilisation of sequence components of standard current and voltage measurement at a primary substation and they do not require new devices or measurements in the field. Both methods are tested with a simulation of a network based on data provided by a Norwegian network operator and their prediction performances were investigated considering different scenarios on fault resistance, load asymmetry and measurement errors. Both methods performed well and showed good accuracy. The simulation results are promising and strengthens the prospect of further testing of the methods in real networks and adoption of the methods for a real-world implementation. • Novel earth fault location method for ungrounded ring operating MV networks. • Earth fault location method utilising the ratio of change in negative sequence current. • Earth fault location by utilising the ratio of change in zero sequence current & voltage. • Equations for sequence currents & division of zero sequence current in ring network. • Robust fault location against fault resistance, load asymmetry and measurement errors.

A comprehensive empirical study on bug characteristics of deep learning frameworks

Deep Learning (DL) frameworks enable developers to build DNN models without learning the underlying algorithms and models. While some of these DL-based software systems have been deployed in safety-critical areas, such as self-driving cars and medical diagnostics, for DL frameworks, characterizing their bugs and thus helping researchers to design specific quality assurance techniques become desperately needed. Our research aims to characterize bugs typical of DL frameworks at the source code level for an in-depth analysis of bug symptoms, root causes, and bug fixes. In this way, we hope to provide insights for researchers to design automatic quality assurance techniques, such as automatic repair techniques and fault location techniques, applicable to DL frameworks and DL-based software systems. We started by summarizing the DL framework reference architecture and proposing the DL framework bug taxonomy. Then, we mined 1,127 DL framework bug reports from eight popular DL frameworks and labeled the bug types, root causes, and symptoms. Finally, we discussed the bug characteristics and explored how developers could possibly deal with these bugs. Our main findings are: (i) DNN model building bugs and general type bugs accounted for one-third of the total defects. (ii) DNN model building bugs are more prone to algorithm logic constraints, internal API errors, and data/numerical errors. (iii) Fifteen bug-fixing patterns are summarized, providing reference for common DL framework bug repair and future research on the development of automatic DL framework bug detection tools. By analyzing the bug-fixing changes, we characterize the occurrences, root causes, symptoms, and fixing of these bugs. The study results have provided researchers with insights into how to ensure DL framework quality and presented actionable suggestions for DL framework developers to improve their code quality.

Distribution Management Systems for Smart Grid: Architecture, Work Flows, and Interoperability

The smart grid integrates advanced sensors, a twoway communication infrastructure, and high-performance computation-based control. The distribution management systems for smart grid include several functions for manipulating legacy voltage control devices and distributed energy resources through closed-loop volt/var control, leading to wide-area regulation of voltages in the presence of fluctuating power. The other primary distribution network analysis application is concerned with automatic fault location and service restoration following fault events, aiming to provide the grid with autonomous intelligence for self-healing. Communication technologies are vital to enable the computing applications of distribution networks, whether they work in centralized or distributed modes. This paper presents the state of the art in distribution management system architectures and modern workflows showing data exchange, practical parallel implementations designed to handle large amounts of data, in addition to communication standards that serve as interoperability enablers. It demystifies the relationship between different functions developed independently by power system researchers and shows their operation as a complete system, thus placing them in a better context for future research and development.

Automatic Fault Location Identification and Isolation Method for Smart Distribution Network in Surabaya City

There are various types of fault that can occur in the distribution system network, so it is necessary to identify the location of the fault and isolate the fault in the area of the fault. The city of Surabaya is in preparation for the development of a smart city, so it is necessary to prepare a smart distribution system network system that can identify locations and isolate disturbed areas automatically. This paper describes the reconfiguration process to improve the value of losses in the system which results in a decrease in the value of total line losses after reconfiguration of 313.46 kW from 8 scenarios and includes the effect of adding solar energy to the existing network. The process of identifying the fault location and the isolation process on the Surabaya distribution system network in this paper uses the deep learning method. The fault location is determined based on the voltage and current profile of each bus in the system, while the isolation process is carried out by opening the switch closest to the fault area. In this process, deep learning can provide accurate fault location and isolation results for 6 fault tests.

Transient and steady-state faults location in intelligent distribution networks compensated with D-STATCOM using time-domain equations and distributed line model

Intelligent electric distribution networks compensated with D-STATCOM are active networks in which data of terminals is important for better distribution of energy. Similar to all electricity network devices, energy distribution lines in such networks are also subject to transient and steady-state AC faults, e.g., short circuit. Sections of the network in which the fault occurs are separated by relays. In such a condition, fault location and restoring the normal status of the network are important. Fault location in these networks is performed based on synchronous data of two terminals because data of all buses are transmitted to the main bus In this paper, a time-domain equation method (TDEM) is proposed for locating faults on smart distribution grid (SDG) using distributed line model based on time-domain equations. The proposed method locates the point of transient and steady-state fault in SDG considering DGs and compensators. In this paper, the D-STATCOM and wind power plant are considered in SDG. The suggested method does not need the D-STATCOM and DGs model for applying the algorithm. It only uses data of less than half of the cycle for executing the algorithm. The numerical simulation confirms that the proposed fault location method is accurate and it provides a reliable solution for automatic fault location problem in distribution networks.

Linear and Nonlinear Fault Location in Smart Distribution Network Under Line Parameter Uncertainty

The line parameters of the distribution network (DN) may change because of the atmospheric, structural, and operational conditions. The uncertainty of line parameters can compromise the accuracy of the automatic fault location methods. Besides, arc faults (AFs) may happen in the DN. These faults are difficult to locate in the faulty section because of the nonlinear, asymmetric, and random nature of AF current. In this article, we present a new time-domain fault location (TDFL) method to determine the location of the fault in smart power DN under the line parameters uncertainty. In the suggested method, line parameters' accurate values are determined using a mixed gradient descent particle swarm optimization algorithm. The suggested method's performance is investigated with the help of an IEEE 123-node test feeder in MATLAB (R2018b). The effects of parameter uncertainty, distributed generations operation conditions and modes, different types of AF, various fault distances, resistances, and the fault inception angles are studied. For further evaluation of the proposed method's robustness, two practical tests in the laboratory are carried out. The results confirm that the proposed TDFL method has high accuracy.

A Novel Fault Location Methodology for Smart Distribution Networks

Power distribution networks (PDNs) has played a crucial role in expediting transition towards cleaner and better distributed energy sources. Nowadays, more and more distributed generations (DGs) are used in PDNs which complicates the automatic fault location. This article presents an accurate impedance-based method to determine the fault location for smart PDN in the presence of DGs. In addition, phase domain equations of distributed line parameters are used to enhance the accuracy of fault location. Two types of networks are considered. The first type of network is assumed to be fully observable with μPMU and in the second type there are only a few μPMUs with data loggers on the rest nodes. Load impedances of all nodes are estimated using pre-fault recorded information by present μPMUs and data loggers. The proposed algorithm might suggest several points as possible fault locations for a PDN. To find out the actual location of fault same fault type is simulated for all suggested points. A matching value which is mathematically defined in the article, is calculated using recorded and simulated voltage to determine the actual fault point among all the suggested candidates. The accuracy of suggested method is analyzed against various conditions.

A technique for fault detection, identification and location in solar photovoltaic systems

Due to exponential growth of large-scale PV plants, automatic approaches for PV system protection are gaining prodigious importance. Even with the use of standard protection devices, faults in PV arrays may remain undetected. To address such an important issue, this paper focuses on string level monitoring to develop the functionality of automatic fault detection, location and fault type identification. The fault detection is achieved through the generation of fault indicator signals called residuals and comparison with a pre-set threshold. The automatic identification of fault type is achieved by the development of a procedure reliant on the variations in the string current profiles relative to the type of fault. Finally, the location of faults is estimated through the combination of analytical and regression expressions reliant on fault type, irradiance levels and string current measurements. Various line-line fault cases are tested and verified using the proposed method through simulations and experiments. The proposed method is experimentally evaluated for multiple fault scenarios on an experimental setup located within an existing solar farm to emulate conditions akin to real world solar farms.

High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers

Power distribution networks are vulnerable to different faults, which compromise the grid performance and need to be managed effectively. Automatic and accurate fault detection and location are key components of effective fault management. This paper proposes a new framework for fault detection and location for smart distribution networks that are equipped with data loggers. The framework supports networks with mixed overhead lines and underground cables. The proposed framework consists of area detection, faulty section identification, and high impedance fault location. Firstly, the faulty zone and section are detected based on the operation of over-current relays and digital fault recorders. Then, by comparing the recorded traveling times at both ends of lines, which are related to the protection zone, the faulty line is identified. In the last step, the location of the fault is estimated based on discrete wavelet transform. The proposed method is tested on a 20 kV 13 node network, which is composed of overhead lines and underground cables. The method is tested in both balanced and unbalanced configurations. The obtained results confirm the advantages of the proposed method compared with the current state-of-the art.

A Hybrid Method Based on Optimized Neuro-Fuzzy System and Effective Features for Fault Location in VSC-HVDC Systems

One of the most important issues with HVDC systems are the occurrence of various faults that can lead to considerable electrical power losses, serious damage to expensive equipment and huge financial losses. Hence, it is highly required to design an accurate and automatic fault location method in HVDC systems for maintaining uninterrupted supply of energy and protecting sensitive equipment such as rectifiers and inverters. Accordingly, this paper proposes a new hybrid system based on adaptive neuro-fuzzy inference system (ANFIS) with optimal parameters and Hilbert-Huang (HH) transform for fault location in voltage sourced converter-HVDC (VSC-HVDC) systems. The proposed fault location method consists of three major sections. In the first section, HH transform is applied to extract new features from current signal. In the second part, ANFIS uses the extracted features to estimate the fault location in transmission lines. Learning algorithm determines the accuracy and efficiency of each machine-learning algorithm. In the third section of the developed system, enhanced version of particle swarm optimization (PSO) algorithm named chaotic dynamic weight PSO (CDWPSO) algorithm is implemented as learning algorithm to train the ANFIS. The developed fault detection and location system was tested on a VSC-HVDC system with 250 km length and the obtained results using MATLAB simulations have shown that combination of new features, and CDWPSO-based ANFIS has high accuracy in fault detection and location in VSC-HVDC systems. High fault location accuracy, robust performance of neuro-fuzzy system, optimal training of ANFIS, extraction of novel effective features from current signal and fault location only with six features are the main contribution of the developed system.

Automatic fault isolation and restoration of distribution system using JADE based Multi-Agents

This paper proposes a solution for automatic service restoration along with automatic fault location and isolation of the faulty sections in feeder in a power distribution system. A Java agent development environment-based multiagent system (MAS) is proposed to solve the problem of automatic service restoration in smart grid distribution systems. The agent-based solution development is discussed in detail and the MAS application to solve power restoration problem is elaborated in this paper. A study is done on a modified IEEE 33 bus system and the solution is implemented in the Velachery substation of the Tamilnadu electricity board. The results prove that fast and effective fault location, isolation and service restoration is achieved using the proposed solution.

FAULT LOCATION STRATEGIES ON POWER CABLES USING OFFLINE METHODS INCLUDE TIME DOMAIN REFLECTOMETRY ( TDR)

The purpose of this study is to investigate the feasibility of offline fault location and state of health technologies for airport series circuits used to provide power to light fixtures on the runway. In current series circuits, only some ground faults can be detected and no automated fault location or state of health features are available. Fault location is currently performed with an isolation tester and a”divide and conquer”-methodology which is very time consuming and personnel intensive.

Automated Fault Location on Power Distribution Lines using Artificial Neural Networks

The work aims to arrive at an accurate estimation of fault location in power Distribution Networks (DNs) using the potentialities of artificial neural networks. For every fault plausible on feeders and distributors of DNs, detailed fault data recording is available only at a common place called distribution substation. In this paper, effort was made to train the Artificial Neural Networks (ANNs) with this plausible common fault data to arrive at an estimation of type of fault and locus of fault. Two ANNs were trained for this task of fault location on an IEEE test case, which was modeled and simulated in MATLAB Simulink. One ANN was dedicated for fault classification to ascertain the specific type of fault; another ANN for detecting the faulted line segment and pinpointing the location on that faulty section. In all, 550 fault combinations were triggered on this simulated IEEE test DN and fault data (voltage and current information) was generated for training and testing of ANNs. The training and testing results clearly demonstrated good degree of accuracy in detecting the correct fault type and faulty section, and locating a closer fault position. This study enables the substation engineer to estimate this fault information sitting in the substation, without actually patrolling or inspecting the affected areas. With this estimation, the maintenance crew can rush to the affected spot with minimum delay to repair and restore the power supply.

Automated Fault Location and Isolation in Distribution Grids With Distributed Control and Unreliable Communication

This paper presents the analysis and simulation of the performance of fault location and isolation (FLI) in an automated power distribution feeder. This system is composed of two coupled networks: a power system consisting of a distribution feeder with multiple load buses, and an unreliable communication network of the distributed intelligent agents in the system, namely, the substation automation and fault protection units separating segments of the feeder. We provide a complete specification of a distributed algorithm for FLI and an exact characterization of the time from the occurrence of a fault to its location and isolation. Both apply to a distribution feeder with an arbitrary number of buses. These models are then refined into a hybrid simulation that combines three models executing in parallel: a power system model based on dc power flow, a distributed automation system model for the intelligent agents constructed using the IEC 61499 distributed automation standard, and an abstract communication network model that unreliably links the physically distributed agents. The results demonstrate the effect of communication network reliability at two levels of design abstraction, the correspondence of results at the two levels, and the use of a modern cosimulation framework to verify the performance of distributed smart grid automation algorithms.

Criticality Analysis of Failure to Communicate in Automated Fault-Management Schemes

The effectiveness of various functions in a smart distribution grid relies on a reliable communication system. These functions are normally developed based on specific automation schemes. The majority of automated fault-management schemes (AFMSs) requires a dependable communication system to accomplish their intended functions. In a smart distribution grid, the AFMSs are responsible for automatic fault detection, location, isolation, and service restoration activities. Failure to communicate among various communicating devices involved in the AFMS may have a considerable impact on the reliability of electricity service delivered to the customers. This paper aims to quantitatively evaluate the reliability impact of failure to communicate in the AFMS. A highly loaded urban distribution network equipped with AFMS is utilized for directing the quantitative reliability case studies. The study results show that the service reliability can be significantly affected if the communication system fails to operate successfully.