Accurate Fault Location Research Articles

UHVDC transmission line diagnosis method for integrated community energy system based on wavelet analysis

With the large-scale development of renewable energy power, China has faced with the challenges of the reverse regional distribution of wind and solar resources and power load, as well as the intermittency and randomness of renewable energy power. Therefore, China is vigorously developing ultra-high voltage direct current (UHVDC) transmission technology to solve the problem of insufficient flexibility caused by the uncertainty of renewable energy and the fluctuation of multi-energy loads in integrated community energy systems. UHVDC plays an increasingly pivotal role in the west-east transmission system in China’s power system due to its high transmission capacity and long transmission distance. Once the fault occurs in the ultra-high voltage direct (UHVD) transmission line, quick and accurate fault location identification is of great significance. Hence, this paper proposes a UHVDC transmission line diagnosis method based on wavelet analysis for integrated community energy systems. Wavelet transform (WT) is used to decompose the transient signal on a multi-scale, and then power systems computer-aided design (PSCAD) software is utilized for simulation calculation to obtain the singular spectrum entropy of each layer and facilitate wavelet transformations for signal denoising with advanced tools such as MATLAB. The prediction results can distinguish outside the rectification side fault, within the rectification side fault, and outside the inverter fault with an accuracy of 100%. A large number of simulations demonstrate that combining singular spectrum entropy with support vector machines (SVM) has emerged as a robust technique for integrated community energy systems, suggesting its potential as a standard method in UHVDC transmission line diagnosis. This study is of significant reference for realizing the complementarity of multiple types of power supply and ensuring a reliable power supply.

A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors

A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors

A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids

DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However, the reliable operation of DC microgrids relies on the early detection and location of faults to ensure an uninterrupted power supply. This paper aims to develop fast and reliable fault detection and location mechanisms for DC microgrids, thereby enhancing operational efficiency, minimizing environmental impact, and contributing to resource conservation and sustainability goals. The fault detection method is based on compressed sensing (CS) and Regression Tree (RT) techniques. Besides, an accurate fault location method using the feature matrix and long short-term memory (LSTM) model combination has been provided. To implement the proposed fault detection and location method, a DC microgrid equipped with photovoltaic (PV) panels, the vehicle-to-grid (V2G) charging station, and a hybrid energy storage system (ESS) are used. The simulation results represent the proposed methods’ superiority over the recent studies. The fault occurrence in the studied DC microgrid is detected in 1 ms, and the proposed fault location method locates the fault with an accuracy of more than 93%. The presented techniques enhance DC microgrid reliability while conserving renewable resources, vital to promoting a greener and more sustainable power grid.

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points.

A Leakage-Current Vectors Fault Location Method for Undersea Observation Networks With Poor Data Quality

For undersea observation networks, accurate fault location is required to reduce repair costs and time. A main technical challenge for this task is the presence of poor-quality data caused by the cascading failure from undersea power networks to the sensors and communication networks. This paper proposes a leakage-current vector (LCV) based fault location method for the negative-pole DC network to address this issue. LCVs are obtained through the nodal admittance matrix considering the electrodes which react in the seawater to provide the return path for the negative pole. The attribute of LCVs is used to identify the anomalous, corrupted and missing data. A novel KCL-based data imputation method is proposed to correct the LCVs, which are compared with measurements to obtain the fault distance and fault resistance. The simulation model of north-east pacific time-series undersea networked experiments (NEPTUNE) is established in PSCAD/EMTDC to validate the proposed method. The results show a 23% improvement in fault location accuracy compared with data-interpolation-based methods.

Fault Signal Perception of Nanofiber Sensor for 3D Human Motion Detection Using Multi-Task Deep Learning

Once a fault occurs in the nanofiber sensor, the scientific and reliable three-dimensional (3D) human motion detection results will be compromised. It is necessary to accurately and rapidly perceive the fault signals of the nanofiber sensor and determine the type of fault, to enable it to continue operating in a sustained and stable manner. Therefore, we propose a fault signal perception method for 3D human motion detection nanofiber sensor based on multi-task deep learning. First, through obtaining the fault characteristic parameters of the nanofiber sensor, the fault of the nanofiber sensor is reconstructed to complete the fault location of the nanofiber sensor. Second, the fault signal of the nanofiber sensor is mapped by the penalty function, and the feature extraction model of the fault signal of the nanofiber sensor is constructed by combining the multi-task deep learning. Finally, the multi-task deep learning algorithm is used to calculate the sampling frequency of the fault signal, and the key variable information of the fault of the nanofiber sensor is extracted according to the amplitude of the state change of the nanofiber sensor, to realize the perception of the fault signal of the nanofiber sensor. The results show that the proposed method can accurately perceive the fault signal of a nanofiber sensor in 3D human motion detection, the maximum sensor fault location accuracy is 97%, and the maximum noise content of the fault signal is only 5 dB, which shows that the method can be widely used in fault signal perception.

Исследование точности определения места повреждения воздушных линий по данным от устройств синхронизированных векторных измерений различных классов и производителей

The technology of synchronized phasor measurements has been widely used in the Russian power system to analyze the parameters of steady-state electrical power modes and to record electro-mechanical transient processes. Issues on fault location based on synchronized phasor measurements have mainly been discussed in foreign publications. A significant drawback of most research papers on this issue is simplified modeling of both overhead lines and current and voltage measurement channel as well as digital filters of phasor measurement units (PMUs). The goal of this research is to develop a fault location algorithm based on synchrophasor measurements and to study its accuracy by using specialized equipment including a real-time digital simulator (RTDS), PONOVO current and voltage amplifiers and production-grade PMUs. A PMU-based double-ended fault location algorithm is developed using long-line equations and well-known electromagnetic transient theory concepts. Currents and voltage oscillograms of both overhead transmission line ends are modelled in the MATLAB/Simulink software package. These oscillograms are saved as COMTRADE files and played back using the RTDS hardware and all-in-one software package RSCAD. In addition, the study uses two production-grade PMUs, the first one is TPA-02, and the second one is a merging unit ENMU acting as a PMU, thus imitating PMUs at the line terminals. To time-align all the measurements and to aggregate PMU data frames, various auxiliary software such as PMU Connection Tester, and hardware is used. A double-ended fault location (FL) method utilizing synchrophasors under a fault has been developed. The method is based on an overdetermined system of nonlinear equations that describes physical processes in an overhead power line and can be applied under various fault types. An integrated study of the efficiency of the developed FL method has been conducted. FL errors have been computed using production-grade PMUs ENMU and TPA-02, along with the RTDS and other equipment. The authors have considered the case of configuring PMUs of different classes at the overhead line terminals, and different phasor reporting rates as well. The conducted experiments make it possible to reveal that the fault location errors do not exceed the thresholds imposed by the standard STO 56947007-29.120.70.241-2017 in 88 % of all the analyzed fault scenarios. The developed FL method makes it possible to achieve the accuracy required by regulatory guide in calculating the distance to the short circuit point in most of the cases examined. Based on the results of numerical experiments for various types of fault cases, it can be concluded that the PMU class and phasor reporting rate do not have a significant impact on the FL accuracy, provided that the fault duration is enough for the PMU filter to approach a steady output.

Detection and localization of fault in DC microgrid using discrete Teager energy and generalized least square method

PurposeThis paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.Design/methodology/approachIt is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.FindingsDTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.Originality/valueVerification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.

Fault Location Method for Distribution Network Using an Additional Inductance Strategy

In distribution networks, time asynchrony exists between the phasor measuring unit (PMU) at both ends of a line, and the effective measurement time of the devices is short, leading to insufficient accuracy in phasor measurements. This paper proposes a fault location method for distribution networks that employ an additional inductance strategy to address the limited location accuracy caused by time asynchrony and the inadequate accuracy of phasor measurement devices. The method enhances the stability and accuracy of phase measurement by connecting an additional inductance after the online circuit breaker, thus extending the effective measurement time. It uses the symmetrical component method to obtain the positive-sequence and negative-sequence networks following a fault. Time asynchrony is treated as an equivalent asynchronous phase angle, which is then applied to the positive and negative-sequence voltage components. The impact of time asynchrony is mitigated by compensating for the phase angle difference using the ratio of the positive-sequence voltage component to the negative-sequence voltage component. This approach provides the fault location function and has improved the accuracy of fault location, which is advantageous for rapid fault repair.

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.

Multi‐agent protection scheme for microgrid using deep learning

AbstractProducing clean energy and feeding critical loads in islanding mode are the main reasons for interest in microgrids. Different operation topologies of microgrids make traditional protection schemes inefficient. This paper proposes a multi‐agent protection scheme in which each protection agent can detect different fault events and isolate faulty phases at a fast rate. A unique algorithm is utilized for determining fault location in microgrids and system operators are informed accordingly. Microgrids have various operation modes due to the stochastic behavior of distributed generators and different topologies. Here, a significant number of operating conditions of the studied microgrid are considered. These operation conditions are simulated in the DIgSILENT Power Factory, and different parameters are stored. Raw measured parameters need to be pre‐processed by a signal processing method in MATLAB. Discrete wavelet transform is chosen for this purpose. Deep learning is used as a machine learning technique due to the various operation modes of the microgrid. Deep neural networks are constructed using Python programming language. The proposed scheme ensures high accuracy in fault detection and fault location in the microgrid, as well as fault isolation in different operation conditions.

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.

Accurate Fault Location Algorithm for Untransposed Transmission Lines Based on Network Phasor Equations in Positive-, Negative-, and Zero-Sequences Domain During Fault

Accurate Fault Location Algorithm for Untransposed Transmission Lines Based on Network Phasor Equations in Positive-, Negative-, and Zero-Sequences Domain During Fault

Research on Phase-to-phase Fault Location Method of Active Distribution Network

Due to the weak feed characteristic of active distribution networks, the sensitivity of the original fault phase selection method and the accuracy of fault location are decreased. Most of the existing fault section location methods focus on the phase selection and location of the earth fault, and there is a lack of relevant research on the location of the fault section to the phase-to-phase fault. Moreover, most of the methods need to install the signal measuring device in the system as large as possible, which is not economical. The operation condition of active distribution networks is complicated, the fault characteristics are weak, and the location result is easily affected by non-fault disturbances such as DG access capacity and location. In this paper, the phase-to-phase fault of active distribution networks is taken as the research object. Firstly, phase current and zero-sequence current combined criterion is used for fault phase selection, and then combined with the phase-frequency characteristics of mode impedance of distribution line and photovoltaic power line, a fault section location method based on transient characteristic frequency band is proposed, which is not affected by non-fault disturbance such as photovoltaic control strategy and DG access capacity and location. Simulation results show that the method is effective.

Fault Location of Grid-connected Microgrid Lines Based on AVMD and Double-ended Traveling Wave Ranging

To enhance the accuracy of fault location in microgrid lines, a fault location method based on adaptive variational mode decomposition (AVMD) and double-ended traveling wave ranging is proposed. Firstly, a microgrid radial structure model, incorporating wind, light, and water systems, is established. The parameters of variational mode decomposition are optimized by using the Aquila optimizer (AO) algorithm, allowing the original fault signal to be decomposed into multiple modal components using AVMD. Only a limited number of modal components retain the fault signal information. The effective weighted peak relevance index (EWPR) is employed to select the sensitive modal components, identified as the four modes that best preserve the fault information, for signal reconstruction. Finally, the reconstructed signal is applied for fault localization. Simulation results indicate that the proposed method is scarcely influenced by the fault type, exhibiting superior accuracy compared to the single-ended traveling wave method.

Ground fault identification and location of autotransformer traction network for high‐speed railway based on multi‐terminals synchronous measure information

AbstractWith the rapid development of high‐speed railway, the auto‐transformer traction network (ATN) is also expanding. However, due to the improved performance of electric multiple units (EMUs) and the non‐linear relationship between impedance and ground fault distance, distance protection has limited coverage and cannot accurately locate the fault distance by measuring impedance. This paper presents a model of the ATN considering the presence of EMUs, which effectively demonstrates the characteristics of current distribution under the ground faults. Furthermore, a ground fault identification method based on multi‐terminal currents is proposed to enhance the accuracy of fault identification and differentiate faults and EMUs. The mapping relationship between current distribution and fault location is derived, and an accurate fault location method is proposed. Compared to existing methods, this method is not affected by EMUs and AT parameters. The fault identification and location scheme for ATN employs multi‐terminal synchronous information. Finally, through simulation and experimental setups, case studies have been conducted to verify the accuracy and robustness of the proposed method in various fault scenarios. Certainly, this research has significant application prospects in fault analysis and detection for HSR.

A Fault Location Method for Medium Voltage Distribution Network Based on Ground Fault Transfer Device

The arc suppression device based on ground fault transfer (GFT) has been preliminarily applied in the medium voltage distribution network (MVDN). An accurate travelling wave (TW) fault location method is proposed to extend the use of the ground fault transfer device. D-PMU is used as a travelling wave detection tool to record the transient voltage travelling waves of fault grounding and bus active grounding during arc suppression. Then, the faulty section is identified through the time difference of travelling wave arrival at the upstream and downstream measurement points. On this basis, the fault location equations of the arrival time and distance of the upstream travelling wave are established, and an accurate fault location method based on the arrival time difference of the travelling wave is proposed. The simulation model is established by PSCAD/EMTDC, and the results show that the method has high location accuracy, and the absolute error is less than 30 m. It is not affected by the TW velocity, the fault conditions, or the distributed power sources.

End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN

An end face attitude detection system for special steel bars is designed to solve the problem of defect localization for steel bar grinding. A circle detection method based on improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed for calculating special steel bars’ end face attitude. Firstly, the images are subjected to edge detection, connected region marking, and improved DBSCAN in accordance with the image characteristics. After that, the arcs belonging to the same circle are clustered into the same category to create virtual connected regions. Then, circle parameters of a virtual connected region are clustered using an improved DBSCAN algorithm. The actual circle parameter is obtained by calculating the centroid of each category. Finally, the vector is generated under the set coordinate system, passing through the center of the circumcircle of the steel bar end and one endpoint of the two-dimensional code, and the angle of the vector is calculated to determine the attitude of the special steel bar’s end face. The experimental results demonstrate that the method can obtain an attitude angle resolution of 0.2 degrees with an error range of ±0.1 degrees. This will provide accurate defect localization support for the digitization and intelligence of the grinding platform on the special steel bar production line.

Accurate Fault Location Method Based on Time-Domain Information Estimation for Medium-Voltage Distribution Network

The sampling rate of the wide-area synchronous phasor measuring device (D-PMU) in the distribution network is insufficient, and the localization sensitivity of the traditional localization method based on the time-domain Bergeron equation in the distribution network is insufficient. In this paper, an accurate location method of the distribution line grounding fault based on the time-domain’ synchronous information calculation is proposed to solve the problem of limited location accuracy caused by a low sampling rate and the insufficient sensitivity of traditional methods. The method preprocesses the measurement data through low-frequency time-domain signal reconstruction and cubic spline interpolation. The fault current’s different location criterion is constructed by using the voltage and current constraints at the fault point. By calculating the fault current difference at a limited number of calculated points, the accurate fault location under a low sampling rate is realized, which is beneficial to the rapid maintenance of faults and the shortening of the power outage time.

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.