Cable Monitoring Research Articles

GSM Based of Unclad Fibre Temperature Sensor for Electrical Transmission Cable Monitoring System

This paper presents a temperature monitoring system for an electrical transmission cable utilizing a global mobile service (GSM)-based unclad fibre sensor. The developed system consists of GSM module, Arduino controller, unclad plastic optical fibre (POF) sensor, light emitting diode (LED) and photodetector. The unclad POF sensor was chosen due to its immunity towards electromagnetic interference as well as its high sensitivity performance. In this work, the transmission cable hazardous temperature is set at 70oC, which is the maximum operating temperature value provided by the National Electrical Code (NEC) NFPA 70. For system characterization, temperature of the cable was varied from 30oC to 70oC. Photodetector output voltage for this temperature variation were recorded at 660 nm and 770 nm operating wavelength. The recorded data shows that the sensor sensitivity performances at 660 nm wavelength are found superior than 770 nm wavelength, which are 0.9710mV/oC and 0.9699mV/oC respectively. As for the GSM communication system, the system was characterized based on accuracy of the information send via GSM text message and its delay. The developed system offers economical and practical monitoring system to be adopted for transmission cable temperature monitoring in remote area.

Development and engineering application of fiber bragg grating intelligent cable in large-span hyperbolic parabolic spatial cable network

In order to accurately control the prestress force of cables in long-span cable net structures, a new type of fiber Bragg grating (FBG) intelligent cable was developed. The FBG sensor was directly encapsulated inside the cable, and the sensing performance of the intelligent cable was verified by the cyclic tensile tests. The results show that the sensitivity coefficients of FBG monitoring are basically consistent, and the linearity deviation of the fiber Bragg grating is less than 4.67 %. Based on a real project Shunde Desheng Sports Center, a 114 m long-span saddle-shaped cable net structure model was established using finite element analysis, and the distribution and correlation of internal forces of cables in different tension stages were calculated and compared with the actual monitoring results of intelligent cables. The analysis results indicate that the force changes of intelligent cables is basically consistent with the value of finite element simulation. According to the data of long-term intelligent cable monitoring, the change of cable force caused by the temperature deformation of the steel ring beam is much greater than the influence of the temperature rise and fall of the cable itself on the cable force. Intelligent cables can provide a reference for cable force monitoring of long-span cable net structures.

Power cable monitoring method based on UHF‐RFID and deep learning in edge computing environment

AbstractThis research addresses the challenge faced by most existing prediction methods in handling nonlinear data of cables. Furthermore, it proposes a novel power cable monitoring method utilizing UHF‐RFID and deep learning within an edge computing environment, specifically targeting the currently suboptimal wireless monitoring of cables. First, based on edge computing, a power cable monitoring system is designed to migrate the analysis of massive data to the edge of the network to improve the monitoring efficiency. Then, the temperature sensing chip and RFID chip were integrated to design a UHF‐RFID temperature tag, which was fixed at the cable temperature measurement point to achieve passive wireless monitoring of the cable. Finally, the parameters of the GRNN model are optimized using the beetle antennae search algorithm, and the EEMD decomposed data is input into the BAS‐GRNN model for learning to output temperature prediction results. Based on the establishment of an experimental platform, the method was demonstrated, and results showed that the maximum error between the UHF‐RFID temperature tag temperature measurement results and the thermocouple was within 0.3°C, and the average relative error of the proposed method was only 0.01, which can meet the accuracy requirements of actual monitoring of power cables.

Real-Time Monitoring of Cable Sag and Overhead Power Line Parameters Based on a Distributed Sensor Network and Implementation in a Web Server and IoT.

Based on the need for real-time sag monitoring of Overhead Power Lines (OPL) for electricity transmission, this article presents the implementation of a hardware and software system for online monitoring of OPL cables. The mathematical model based on differential equations and the methods of algorithmic calculation of OPL cable sag are presented. Considering that, based on the mathematical model presented, the calculation of cable sag can be done in different ways depending on the sensors used, and the presented application uses a variety of sensors. Therefore, a direct calculation is made using one of the different methods. Subsequently, the verification relations are highlighted directly, and in return, the calculation by the alternative method, which uses another group of sensors, generates both a verification of the calculation and the functionality of the sensors, thus obtaining a defect observer of the sensors. The hardware architecture of the OPL cable online monitoring application is presented, together with the main characteristics of the sensors and communication equipment used. The configurations required to transmit data using the ModBUS and ZigBee protocols are also presented. The main software modules of the OPL cable condition monitoring application are described, which ensure the monitoring of the main parameters of the power line and the visualisation of the results both on the electricity provider's intranet using a web server and MySQL database, and on the Internet using an Internet of Things (IoT) server. This categorisation of the data visualisation mode is done in such a way as to ensure a high level of cyber security. Also, the global accuracy of the entire OPL cable sag calculus system is estimated at 0.1%. Starting from the mathematical model of the OPL cable sag calculation, it goes through the stages of creating such a monitoring system, from the numerical simulations carried out using Matlab to the real-time implementation of this monitoring application using Laboratory Virtual Instrument Engineering Workbench (LabVIEW).

Artificial Intelligence Assisted Smart Self‐Powered Cable Monitoring System Driven by Time‐Varying Electric Field Using Triboelectricity Based Cable Deforming Detection (Adv. Energy Mater. 27/2024)

Artificial Intelligence Assisted Smart Self‐Powered Cable Monitoring System Driven by Time‐Varying Electric Field Using Triboelectricity Based Cable Deforming Detection (Adv. Energy Mater. 27/2024)

Secure and Efficient Federated Learning for Smart Optical Cable Monitoring Systems

This study presents a pioneering federated multi-modal data classification model tailored for smart optical cable monitoring systems. By harnessing federated learning techniques, the model ensures data privacy while achieving performance on par with centralized models. Through comprehensive experiments spanning various modalities, including vision and auditory data, our approach showcases promising outcomes, as evidenced by accuracy and precision metrics. Furthermore, comparative analyses with centralized models highlight the superior data security and reduced network strain offered by federated learning. Moreover, we delineate the design and deployment of a smart optical cable monitoring system leveraging edge computing, accentuating the pivotal role of information technology in elevating operational efficiency within the cable monitoring domain. Through meticulous analysis and simulations, our proposed system adeptly monitors environmental variables, thereby bolstering safety and efficiency in smart optical cable monitoring applications.

Application of a simple two-point measurement strategy with emphasizing low-frequency signals to the force monitoring of cables

When dealing with vibration tests of tensioned suspenders and cables, signals containing relatively high frequency components in comparing to the fundamental frequency are normally required in order to carry out reliable diagnoses for force estimations. Meanwhile, the necessity of obtaining a precise and broadband spectrum of the vibration signals would inevitably need to acquire relatively detailed time responses. One typical difficulty, which arises when incorporating vibration analyses into monitoring tasks, is the inefficiency caused by the post- processing for the huge amounts of time data. In this work, a set of simple yet effective formulas associated with two-point measurements will be briefly addressed. The proposed formulas are derived based on the continuous formulations of the string model, and the corresponding applications to the tension estimations of slender members will be demonstrated using numerical and experimental data related to stayed cables of various restrained scenarios and linked-suspenders as well. The purposes of this work are twofold; firstly to introduce the potential usefulness of the proposed measurement strategy to form effective schemes for the tension assessments; secondly to evolve an efficient incorporation of wireless measuring techniques without the need of processing cumbersome time data which are the common consequences the analysts would encounter in the analysis tasks. Finally, this work aims to highlight practical ideas in guiding the use of the proposed techniques in the monitoring of cables/suspenders in field.

Artificial Intelligence Assisted Smart Self‐Powered Cable Monitoring System Driven by Time‐Varying Electric Field Using Triboelectricity Based Cable Deforming Detection

AbstractCable monitoring is essential for the prevention of machine malfunctions as machines are operated dynamically. Traditional methods of cable monitoring, conducted through portable or fixed devices, possess the inherent limitations in real‐time damage detection and precise location identification. Herein, a self‐powered, smart cable monitoring system is proposed, utilizing a triboelectric nanogenerator (TENG) as a sensor for the cable and an electric field energy harvester (EFEH) as a power source of the system. Also, the generated electrical outputs from the EFEH are theoretically and experimentally investigated according to the EFEH‐layer numbers, and the optimal number of EFEH‐layers is determined, generating an average electrical power of 2.04 mW. Through hybridization of TENG and EFEH, a synergistic effect is confirmed, resulting in a remarkable 155% enhancement in electrical energy. Consequently, the proposed system is endowed with self‐powered wireless communication capabilities. Additionally, employing a pre‐trained long short‐term memory‐based model, the system can predict the remaining lifespan of the cable with an accuracy rate of 93.7%. Considering these results, the proposed system demonstrates significant potential for industrial cable monitoring applications in the near future.

Improving power cable partial discharge pattern recognition through gustafson-kessel fuzzy clustering techniques

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring.

Enhancing Cable Vibration Measurement at Long Distances Through Super-Resolution Reconstruction and Target Foreground Segmentation

This paper presents a comprehensive non-contact computer vision-based system for monitoring cable vibrations in cable-supported bridges, addressing challenges related to low image resolution and feature extraction difficulties at long distances. The proposed system utilizes deep learning techniques to enhance cable vibration recognition accuracy and offers a practical solution for cable monitoring without the need for target assistance. The core of the system is a novel two-stage model, which combines a super-resolution (SR) video reconstruction algorithm with state-of-the-art Resnet-34 and Swin-B models for precise target foreground segmentation. This approach significantly improves the recognition of target details and enhances the accuracy of cable vibration data in monitoring videos. Furthermore, a phase-based motion estimation (PME) algorithm is employed for precise cable vibration measurement. Field tests conducted on two cable-supported bridges validate the effectiveness of the system. The results demonstrate superior accuracy and noise immunity compared to traditional methods, achieving sub-pixel level precision with a maximum error rate below 2%. This system represents a significant advancement in non-contact structural health monitoring for long-distance cable vibration monitoring in cable-supported bridges.

Research on an Intelligent Identification Method for Cable-Stayed Force with a Damper Based on Microwave Radar Measurements

Aiming at the shortcomings of traditional contact cable force monitoring technology in accuracy, efficiency, and applicability, an assessment method based on microwave radar measurements is proposed to measure a sloping cable with a damper for lengths greater than 200 m in this study. A formula for calculating the cable-stayed force with a damper is derived, and an intelligent cable force monitoring platform is developed based on cloud technology. Based on the Chongqing Nanjimen Railway Bridge, a real bridge test was carried out. It was indicated that the microwave radar method could be used to freely adjust the measurement angle and possessed high applicability and penetration. It significantly improved the measurement accuracy and efficiency of cables with a damper for lengths greater than 200 m. It has good application value for the solution of the problems of complicated operation and high costs in the monitoring of cables with a damper. The formula for calculating the cable force with a damper was proven to be reliable and accurate when compared to the results of direct calculation and the equivalent cable length method. It was able to significantly reduce the calculation error of the cable force caused by the influence of the damper. Additionally, the intelligent cable force monitoring platform was utilized to enhance the level of digitization, providing technical support for the scientific management and maintenance of bridges.

Fully Automated MV Cable Monitoring and Measurement System for Multi-Sample Acquisition of Artificial Aging Parameters

The Power quality of our networks is influenced by transients, short- and long-duration variations, voltage unbalance, waveform distortions, voltage fluctuations and power frequency variations. These impacts affect the aging rapidity of the electrical equipment and finally the entire electrical network. By partial discharge (PD) and tan(δ) analyses, it is possible to determine the electrical equipment’s condition. The significance of the results depends on the quality of the measurement system and of the data interpretation. Based on these results, a reliable maintenance and investment strategy could be made, and the reliability of the network could be improved. A specially designed aging system for the accelerated aging of MV cables has been developed to point out the most relevant aging parameters and their limits, and, in this way, to upgrade the accuracy of the MV cable diagnostics. In this paper, the system’s “intelligence”, which controls the aging process and acquires, evaluates and stores all necessary data is presented.

Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Power grid monitoring based on Machine Learning and Deep Learning techniques

<p><strong>Background:</strong> In this work, some application examples of machine learning and deep learning techniques in the monitoring of electricity distribution services and infrastructures are proposed. Three different fields of application are considered to highlight the use of techniques based on neural networks: detection and classification of power quality disturbances, monitoring of underground cables in medium voltage lines and diagnosis of joints in high voltage overhead power lines. <strong>Methods:</strong> In the field of power grid monitoring, this work proposes a classification method based on a complex valued neural network to assess working conditions of junction regions in high-voltage overhead lines and insulating materials in medium voltage underground networks. The purpose of this method is to prevent the rupture of joint structures and the abnormal degradation of underground cables via frequency response measurements. This approach allows the direct processing of complex measurements and to reduce the computational effort compared to other methods available in the literature. <strong>Results:</strong> The results obtained in the monitoring of underground cables and joints of high voltage lines guarantee an overall classification rate higher than 90%. In the field of power quality, several deep learning and machine learning methods are proposed to detect the most common voltage disturbances. <strong>Conclusions:</strong> In this paper, an innovative use of widespread algorithms such as convolutional neural networks is proposed with excellent results. Furthermore, the use of a complex-valued neural network in electrical infrastructure monitoring is presented, introducing a minimally invasive classification method that could be instrumental in the transition from corrective to predictive maintenance in the near future.</p>

Research on Deployment Scheme and Routing Optimization Algorithm of Distribution Cable Condition Monitoring Devices

In order to achieve more effective online monitoring of distribution cables, a deployment scheme of the distribution cable condition monitoring devices based on a wireless sensor network (WSN) has been proposed. The proposed deployment scheme can improve the coverage rate and extend the lifetime of the sensor network. Moreover, the clustering method, node density, and node deployment method of the wireless sensor routing algorithm are improved, and based on isosceles triangle non-uniform deployment, a routing optimization algorithm has been proposed to achieve the balance of the energy consumption of each node in the network. Firstly, according to the energy consumption model of the network, the energy consumption of each cluster is calculated, and then by taking the minimum energy consumption of data transmission between clusters as the constraint condition, the optimal clustering distance of the network is solved. Then, according to the scale of network nodes, the density of routing nodes in each cluster is calculated, and the routing nodes in the cluster are deployed by an isosceles triangle. Finally, according to the cluster head election formula, the routing cluster head nodes in each cluster are selected, and the network data information is transmitted to the base station in a multi-hop manner through the routing cluster head nodes. The simulation results indicated that, compared with the traditional routing optimization algorithms, the proposed algorithm could keep the routing coverage at more than 200% all the time, and can effectively balance the energy consumption of nodes, improve the energy utilization efficiency of the routing nodes, and at least extend the lifetime of the network by two times. Moreover, the longer the cables, the more cost savings and the better the life cycle optimization effect of the proposed scheme. In addition, the proposed scheme can greatly reduce the economic cost of network investment, compared with using the demodulator to obtain monitoring data.

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications due to their immunity to electromagnetic interference and multiplexing capability. This paper reviews the current technology readiness levels of FBG sensors for condition monitoring of transformers, transmission lines, towers, overhead insulators and power cables, with the aim of stimulating further development and deployment of fibre-based HV asset management systems. Currently, there are several reported cases of FBG sensors used for condition monitoring of HV assets in the field, proving their feasibility for long-term use in the power grid. The review shows that FBG technology is versatile and can facilitate multi-parameter measurements, which will standardise the demodulation equipment and reduce challenges with integrating different sensing technologies.

A Method for Online Monitoring Data Release of Composite Submarine Cable Based on Horizontal Federated Learning

Conventional online composite submarine cable monitoring data release mostly adopts the method and principle of blockchain dynamic zoning consensus. In the data release process, there are omissions, and it takes a long time to complete the task, which reduces the timeliness of online composite submarine cable monitoring data release. Based on this, a new data publishing method is proposed by introducing horizontal federation learning. First, the online monitoring data of composite submarine cables are collected and preprocessed to eliminate the high-frequency capacitive effect of submarine cables. Secondly, manage composite submarine cable data nodes, transform the status relationship of data nodes, and ensure the quality of subsequent data release. A horizontal federation learning model is established to design the online monitoring data release process. The experimental results show that the new data release method is highly feasible. With the increasing online monitoring data of composite submarine cables, the time required for data release is short, and the timeliness is high.

Event-Driven Non-Invasive Multi-Core Cable Current Monitoring Based on Sensor Array

Current monitoring of multi-core cables plays an important role in various applications since cables are widely utilized in distribution and transmission systems. However, existing current sensing devices which are mostly designed for the single conductor break the cable jacket to measure conductor currents in multi-core cables. Recently various non-invasive sensing devices with reconstruction algorithms are designed for rapid access to currents in multi-core cables but don't make full use of abundant information during current monitoring. Thus, this paper proposes an event-driven non-invasive multi-core cable current monitoring method using an array of magnetic field sensors. Real-time currents of the multi-core cable are determined by solving conductor localization and current measurement problems. Since the observed changes of the magnetic field around the cable are caused by current or position changes, conductor localization is driven by the events which are classified as electrical events and mechanical events respectively. The key idea is to improve the accuracy of conductor localization through effective electrical events. Then current monitoring is achieved by solving current measurement problems using more accurate conductor positions and the measured magnetic field. This method is utilized in the real cable current monitoring, a sustaining decrease of relative current errors is observed, which is below 1% in long-term monitoring.

Ensuring the supply of current to one end of the cable with different wire rope parameters

Issues. Structural cable monitoring systems are widely used to diagnose the condition of cable-stayed bridges. Known methods of non-destructive control of the stressed state cannot in some cases be fully used, or their use is difficult and economically impractical or inefficient. The aim of the study. A model is offered that allows you to ensure the reliability of the rope with many degrees of damage. Method. The most convenient method for monitoring the mechanical parameters of cable ropes is the electrical resistance method. The results. The results of the dependence of the stress and current distribution on the rope parameters are presented. The researched method will increase the reliability, safety and service life of cable-stayed bridges. Scientific novelty. Our work consists in studying the impact of breaking the cables that make up cable ropes. For this, a model was created that allows you to combine several parameters (specific resistance and electrical conductivity, length, number of cables). We considered the case for removing the signal from one end of the cable. Practical significance. It is established that when changing the length of the resistance application and the number of cables, a change in the graph for the undamaged and damaged cable is displayed. The number and row in which the burst occurred were analyzed. In this way, the system that monitors one cable notifies about destruction and makes it possible to prevent it.

Development and sensing performance study of a smart CFRP cable assembled by multi-group anchorage units

ABSTRACT Carbon fiber reinforced polymer (CFRP) can be applied for bridge cables due to its excellent properties. As the important load-bearing structural component, real-time force monitoring of the CFRP cable is required. This paper presents a new smart CFRP cable that combines the self-sensing rods with embedded sensors and the anchorage system using extrusion technology. By embedding optical fiber (OF) and coaxial cable Fabry-Perot interferometer (CCFPI) into CFRP rods respectively, two types of self-sensing rods (CFRP-OF rod and CFRP-CCFPI rod) were fabricated. A new anchorage unit using an extrusion process was proposed as a basic component of smart CFRP cables. Anchorage units holding a CFRP-OF rod and a CFRP-CCFPI rod were tested to obtain their sensing and mechanical properties. Three anchorage units were assembled to form a smart CFRP cable with self-sensing functionality. A verification test was carried out to confirm the capability of monitoring the cable force. The test results demonstrate that the smart CFRP cable composed of multiple anchorage units has good potential in bridge engineering.