Cable Fault Research Articles

Incipient fault identification of distribution networks based on feature matching of power disturbance data

Accurate identification of incipient cable fault is helpful to improve the reliability of power system. This paper proposes an incipient fault identification method for distribution networks cables based on feature matching of power disturbance data. Firstly, based on the power disturbance data provided by the power quality monitor about the incipient faults, a characteristic analysis is performed, and three characteristics F1 to F3 that can distinguish the incipient faults of the cable are extracted. Then, the common abnormal condition of 10 kV system is simulated, and the feature quantity is extracted to establish the feature database. Finally, a topic search algorithm is used to perform correlation matching, so as to quickly and accurately identify incipient cable faults from a variety of abnormal conditions. This method can be used to predict cable faults and evaluate the health status, while providing system operation engineers and dispatchers with advanced situational awareness and the causes of impending failures to reduce accident losses and hazards. Simulation results show that the proposed method has high accuracy.

Investigate the Effect of Artificial Neural Network Parameters to Improve Fault Distance and Impedance Accuracy

This paper investigates the effect of artificial neural network (ANN) parameters against the ANN accuracy on cable fault location. The investigation is conducted through the fault impedance and distance estimations during the occurrence of high impedance fault (HIF) in the distribution system. The measured three-phase voltage and current signals are utilized and fed into the ANN to estimate the fault impedance and distance. The accuracy of the estimated fault impedance and distance is evaluated with respect to the variation of ANN parameters. Based on the analysis, it shows that more accurate results can be obtained by utilizing the optimal value of ANN parameters.

RETRACTED ARTICLE: Monitoring method based on GIS for submarine cable fault data

With the growth and advancement of the times, people have more and more means of obtaining knowledge. In order to further increase the efficiency of information transmission and energy transport, people have started to lay cables on the seabed. The region of the sea is huge. Cable laying wi ll provide people with wider networks of information transmission and space. Various factors are unpredictable when laying submarine cables, making the precise work more complicated. It takes a lot of time and expense to successfully install the submarine cable, so it is important to ensure its safe operation. Combining the construction of submarine cables with geographic information systems may rely on visualization methods to combine geography and seabed map information and provide intuitive data for people to understand the situation of submarine cables. In order to minimize the uncertainty created by the U-boat environment for cable safety, this article incorporates knowledge of geographic information systems and uses electronic maps to detect the conditions of the U-boat in real time, and designs a device that can control U-boat cable accidents and provides cable security assistance.

Detection of Series Faults in High-Temperature Superconducting DC Power Cables Using Machine Learning

A novel method to detect series faults in multi-strand high-temperature superconducting (HTS) power cables, based on monitoring the transmission characteristics as an indirect way to detect the magnetic signature, is reported. The efficacy of the non-destructive detection method was studied using finite element analysis (FEA) and measurements on a model cable with a varying number of disconnected strands. An S-parameter model block was implemented, and the changes in the magnetic signature resulting from the failed superconducting strands in the cable were investigated. The transmission characteristics of the cable were experimentally analyzed with various types of series faults. The S-parameters obtained experimentally on the model cable were analyzed, and the output of the implemented linear time-invariant (LTI) system was evaluated with the experimental data. A machine learning tool was developed to predict the type of series fault with a regression algorithm based on the Scikit-learn library. The reported method has the potential of enhancing HTS power system reliability by providing early indications of series faults in HTS cables.

Defect Detection in Pipelines via Guided Wave-Based Time–Frequency-Domain Reflectometry

In order to improve the performance of the conventional guided wave testing (GWT) with a low sensitivity on damage in pipelines, a new GWT based on time-frequency-domain reflectometry (TFDR) is proposed. TFDR using electromagnetic signals is a diagnostic technique for electrical cables, and the signal is analyzed in both time and frequency domains to enhance the resolution of faults in the electrical cables. Since it is impossible to directly apply the TFDR methodology for electrical cables to the pipelines, this article presents new TFDR methodologies suitable for the ultrasonic characteristics of the pipelines. The proposed technique is demonstrated by experiments using a real-world pipeline. The analysis of the experimental results depending on the changes in the defect size and internal material of the pipeline is provided, and the performance of the proposed technique is confirmed by the experiments.

Submarine Cable Detection Method Based on Multisensor Communication

Submarine cable is widely used in today’s oil industry, and it is a much hidden large‐scale industrial facility, which vigorously promotes the development of people’s lives. With the widespread use of submarine cables based on multisensor communication, as far as the current situation is concerned, this paper makes a report and summary on the research of submarine cable detection method in shallow sea area (sea area within 200 m). According to the implementation of the project and the way of controlling variables, the current common detection modes are planned, fault prediction, fault diagnosis, fault analysis and summary, and experimental data comparison, and then, we can use Brillouin radio frequency to prevent the occurrence of submarine cable fault, and when the fault occurs, we can detect the fault at the first time. The feedback value range of TTSL electromagnetic detection is very stable, and the Brillouin scattering frequency is within the normal fluctuation range. In deep‐sea exploration, TTSL electromagnetic detection can detect faults for submarine cables and will not affect the fault in all aspects of waveform and wavelength. Finally, the best path and future development trend of submarine cable detection method are obtained by analyzing and summarizing the detection data, and a complete scheme plan such as some preventive measures and repair technology is put forward.

A Double-Terminal Traveling-Wave-Based Method Using Novel Noncontact Sensors for Fault Location in Transmission Cable Lines

The burgeoning demand for enhanced power-system safety means that there is increased attention on technologies for the detection and location of cable faults. The double-terminal fault location method, which senses a fault current using a current transformer (CT), is the most effective and widely applied method in industrial practice. However, CT sensors may suffer from magnetic saturation when exposed to a high fault current, resulting in significant location errors, and their sensitivity cannot be adjusted. To address this problem, this paper describes an improved method for the acquisition of fault signal and the identification of fault-wave arrival time in power cables. First, a novel noncontact sensor (NCS) is developed for the detection of electric fields, and its sensitivity-adjustment curves are obtained by theoretical calculation and simulation. Then, based on feedback variational mode decomposition (FVMD) and the Wigner-Ville distribution (WVD), an improved method (the FVMD + WVD method) for the identification of fault-wave arrival time is devised, and its efficacy and accuracy are verified by simulations in Power Systems Computer-Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC) software. Finally, the fault-wave arrival-time detection performance of the NCS is examined in a series of on-site DC and AC experiments based on damped AC technology. The results show that the NCS exhibits feasible and effective performance, and that its response can be appropriately tuned based on its sensitivity-adjustment curves. The FVMD + WVD method is more accurate than several other current methods, as its error is only 0.48%, and thus this method enables the practical location of cable faults. In sum, these findings demonstrate that the NCS and the FVMD + WVD method comprise an improved system for the detection and location of cable faults, which will enhance cable safety in power systems.

COMPARATIVE FAULT LOCATION ESTIMATION BY USING IMAGE PROCESSING IN MIXED TRANSMISSION LINES

Overhead lines are generally used for electrical energy transmission. Also, XLPE underground cable lines are generally used in the city center and the crowded areas to provide electrical safety, so high voltage underground cable lines are used together with overhead line in the transmission lines, and these lines are called as the mixed lines. The distance protection relays are used to determine the impedance based fault location according to the current and voltage magnitudes in the transmission lines. However, the fault location cannot be correctly detected in mixed transmission lines due to different characteristic impedance per unit length because the characteristic impedance of high voltage cable line is significantly different from overhead line. Thus, determinations of the fault section and location with the distance protection relays are difficult in the mixed transmission lines. In this study, 154 kV overhead transmission line and underground cable line are examined as the mixed transmission line for the distance protection relays. Phase to ground faults are created in the mixed transmission line. overhead line section and underground cable section are simulated by using PSCAD/ EMTDC ™. The short circuit fault images are generated in the distance protection relay for the overhead transmission line and underground cable transmission line faults. The images include the R-X impedance diagram of the fault, and the R-X impedance diagram have been detected by applying image processing steps. Artificial neural network (ANN) and the regression methods are used for prediction of the fault location, and the results of image processing are used as the input parameters for the training process of ANN and the regression methods. The results of ANN and regression methods are compared to select the most suitable method at the end of this study for forecasting of the fault location in transmission lines.

Pattern Recognition of Optical Fiber Vibration Signal of the Submarine Cable for Its Safety

Submarine cables are the main sources of power supply for oil rigs; in this regard, they must be assessed and effectively secured. Monitoring of the environment of submarine cables is rare, and cable faults generally require interventions. This article develops the detection and recognition of shock events on submarine cables using the vibration signals of its optical fiber sensor. The vibration signals from the optical fiber are detected by means of phase-sensitive optical time-domain reflectometry ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR). An effective fiber vibration pattern recognition scheme based on the time-frequency-domain characteristics (TFDC) and Support vector machine (SVM) is proposed. The scheme uses the TFDC for the recognition of the fiber vibrations, and the SVM to determine the magnitude of the event. Three different groups of data are collected and processed in real-time. Experimental results from multiple tests carried out on data samples show that the proposed method is valid and reliable for recognizing and determining the magnitude of events on the submarine cable.

Predicting the actual location of faults in underground optical networks using linear regression

AbstractOptical cables are enormous transmission media that carry high‐speed data across transatlantic, intercontinental, international boundaries, and cities. The optical cable is essential in data communication. The cable has become an indispensable component in optical communications infrastructure; hence, conscious efforts are always adopted to prevent or minimize faults in the optical network infrastructure. Typically, tracing fault in the underground optical network has been difficult even though the optical time‐domain reflectometer (OTDR) has been used to measure the distance of faults in the underground fiber cable. The methodologies deployed in the reviewed literature indicate a vast gap between the fault distance measured by the OTDR and the actual distance of fault. This paper observed the difficulties involved in tracing the actual spot of fault in the underground optical networks. The difficulty of tracing these underground faults mostly result in an undue delay and loss of revenue. This research presents a machine learning approach to predict the actual location of a fiber cable fault in an underground optical transmission link. Linear regression in the python sci‐kit learn library was used to predict the actual location of a fault in an underground optical network. The mean square error and MAE evaluation matrix used provided good accuracy results of 0.061291 and 0.080143, respectively. The result obtained in this paper indicates that faults in underground optical networks can be found quickly to avoid the delays in the fault tracing process, which leads to an excessive revenue loss.

Analysis and Design of an LCL–T Resonant DC–DC Converter for Underwater Power Supply

Constant dc current distribution is preferred over dc voltage distribution in under-sea power distribution networks due to ruggedness over long distance cable impedance and faults. DC--DC converters with constant current input have different design constraints from their constant voltage input counterparts. Considering these constraints, an LCL-T resonant network based isolated dc-dc converter is analyzed in this article to operate as a load independent constant voltage source when supplied with constant dc current at the input. It is shown that use of passive rectification for this converter can limit converter operating load range, which is overcome through use of an active rectifier. A three-angle modulation strategy for a dual-active bridge (DAB) based LCL-T resonant converter is presented to achieve minimum VA rating for tank elements, transformer and the bridges. Detailed analysis and design method are provided for a converter regulating its output voltage at 150 V fed from a 1 A source, operating with 250 kHz switching frequency, over the load range of 50 to 500 W. Experimental results from prototype hardware are presented to show the accuracy of the analysis.

Karma İletim Hatlarında Makine Öğrenmesi Yöntemleri Kullanarak Hata Yeri Tahmini

Overhead lines are generally used for electrical energy transmission. Also, XLPE underground cable lines are generally used in the city center and the crowded areas to provide electrical safety, so high voltage underground cable lines are used together with overhead line in the transmission lines, and these lines are called as the mixed lines. The distance protection relays are used to determine the impedance based fault location according to the current and voltage magnitudes in the transmission lines. However, the fault location cannot be correctly detected in mixed transmission lines due to different characteristic impedance per unit length because the characteristic impedance of high voltage cable line is significantly different from overhead line. Thus, determinations of the fault section and location with the distance protection relays are difficult in the mixed transmission lines. In this study, 154 kV overhead transmission line and underground cable line are examined as the mixed transmission line for the distance protection relays. Phase to ground faults are created in the mixed transmission line, and overhead line section and underground cable section are simulated by using PSCAD/ EMTDC ™. The short circuit fault images are generated in the distance protection relay for the overhead transmission line and underground cable transmission line faults. The images include the R-X impedance diagram of the fault, and the R-X impedance diagram have been detected by applying image processing steps. The regression methods are used for prediction of the fault location, and the results of image processing are used as the input parameters for the training process of the regression methods. The results of regression methods are compared to select the most suitable method at the end of this study for forecasting of the fault location in transmission lines. When looking at the method and performance criteria used in the overhead transmission line fault location study, it is the Linear Regression (Robust Linear) method that gives the most accurate results with RMSE 0.017652. When looking at the method and performance criteria used in the underground cable transmission line fault location study, it is the Linear Regression (Stepwise Linear) method, which gives the most accurate results with RMSE 0.0060709. When the accuracy of the method was examined, it was seen that it was higher than other methods.

Optimization of Cable Fault Recognition System Using Particle Swarm Optimization

Optimization of Cable Fault Recognition System Using Particle Swarm Optimization

High voltage underground cable bonding optimisation to prevent cable termination faults in mixed high‐voltage lines

Cable termination fault (CTF) generally occurs due to increasing of electrical force (EF) and thermal effect (TE) in mixed line. EF increases due to sheath voltage (SV), and TE increases due to harmonic distortion (HD) on cable terminations (CTs). Also, SV and HD increase due to zero-sequence current (ZC). In the literature, different bonding methods are used to prevent CTF, but these methods are not sufficient to prevent CTF that is based on ZC. In this study, the modified sectional solid bonding (MSSB) is suggested to prevent CTF. The aims of MSSB method are to restrict EF and TE on CT. Thus, MSSB parameters are optimised to restrict SV and HD, but SV and HD on CT should be known for optimisation of MSSB parameters. Thus, SV and HD are forecasted by using adaptive neuro-fuzzy inference system (Anfis), artificial neural networks (ANNs) and hybrid ANN. Training and forecasting errors of hybrid ANN are less than Anfis and the other ANN methods. Thus, hybrid ANN methods are used as fitness function of optimisation algorithms in MSSB optimisation. When the optimised MSSB method is used for cable bonding, HD and SV on CT are restricted the determined limits to prevent CTF in long mixed line.

Dielectric response measurement on service-aged XLPE cables: From very low frequency to power frequency

Polymeric-insulated cables, especially cross-linked polyethylene (XLPE) cables, are widely used in power system transmission and distribution networks. A cable failure can result in lengthy outages and substantial economic loss to the electrical utilities. One of the primary reasons for cable fault is insulation degradation over the service time. Various onsite electrical, thermal or mechanical stresses can accelerate the aging process of power apparatus and curtail the lifetime [1]–[3]. Additionally, different chemical processes can alter the morphological structure or chemical composition of the insulating material and degrade the dielectric properties [3], [4]. Therefore, from economic and technical points of view, regular condition monitoring and diagnostic testing have become critically important for checking the health of the insulation system before the occurrence of any complete failure. Moreover, diagnostic testing performed at appropriate intervals coupled with proper maintenance helps extend the lifespan of high voltage power equipment and components [5]–[8].

IoT based Underground Cable Fault Detection System

IoT based Underground Cable Fault Detection System

Real-time simulation with an industrial DCCB controller in a HVDC grid

DC breakers and their associated control are seen as important lever for the DC grid expansion. In complement to dynamic studies, as intermediate step towards on-site implementation, factory tests using real control and protection hardware enable to fine tune control sequences, to check the software implementation and then test the coordination and interaction between different devices connected to the same grid.This article presents a hardware-in-the-loop setup for testing industrial DCCB controllers and their interoperability with converter controllers. A hybrid DCCB model suitable for real-time simulation has been developed and then validated against offline model. Industrial DCCB controller functions are described. A set-up of three-terminal DC grid with physical controllers for one MMC converter station and 12 DCCBs is described. The results of two application cases including cable energization and fault clearance are presented and discussed.

X—Ray Digital Image Advanced Processing and Buffer Layer Defect Intelligent Identification of Power Cable

With the rapid modernization of the city, power cable has been widely used in the process of urban construction. In the course of cable operation, cable faults become more and more frequent, which has a great impact on national economy and people’s life. The method of digital X-ray imaging can realize the nondestructive testing of power cable body and obtain clear and intuitive X-ray digital image. But it lacks the advanced processing and defect recognition method of X-ray digital image, and can not directly detect and identify the cable body and defect from the original digital image. Therefore, this paper studies the power cable X-ray digital image advanced processing and buffer layer defect intelligent identification technology. By using gray scale processing technology, the original image gray scale range is compressed to the human eye identifiable range, and then the defect identification is carried out. Then the traditional convolution neural network CNN and the full convolution neural network FCN are used to train the image data to realize the intelligent recognition of the power cable buffer layer defect. The research shows that compared with the traditional convolution neural network CNN, the full convolution neural network FCN proposed in this paper has more clear and intuitive recognition effect.

High-voltage Cable Fault Analysis base on X-ray Image Processing

X-ray digital imaging detection technology is the use of high frequency X-ray machine to the sample transitography, through the imaging detector to collect images, and the detected images after digital processing to the computer for analysis and processing technology. With the help of computer digital image processing technology, the image noise can be effectively reduced, and the contrast and clarity of the image can be greatly improved. The radiation dose and exposure time can be selected according to different materials, which greatly improves the detection efficiency. The detection can display the damage of the external force of the cable more quickly and intuitively, and provide strong support for the development of maintenance programs.

Online Detection of Aircraft ARINC Bus Cable Fault Based on SSTDR

In order to realize the online detection of ARINC bus cable fault, a method of spread spectrum time-domain reflectometry (SSTDR) is proposed in this article, which can minimize the influence between the ARINC bus signal and incident signal, and meet the requirements of aircraft airworthiness. First, the impedance characteristics and reflection characteristics of the aircraft ARINC bus cables are analyzed so that the online detection model based on SSTDR is constructed. Then, the SSTDR incident signal is designed by studying the relationship between the incident signal parameters and the ARINC bus characteristics. Finally, the proposed method is verified through the online detection platform of ARINC 429/825 bus cable fault, and the experimental signals are analyzed in the time domain and the frequency domain, respectively, which indicates that the ARINC bus signal and the incident signal do not interfere with each other. The experimental results prove that the method of SSTDR is effective and feasible to the online detection of ARINC bus cable fault.