Condition Monitoring Of Cables Research Articles

Random Forest Based Optimal Feature Selection for Partial Discharge Pattern Recognition in HV Cables

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature-selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. First, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, and then, a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network and Support Vector Machine. Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus.

Modelling and experimental validation of EM wave propagation due to PDs in XLPE cables

Monitoring of electromagnetic (EM) waves due to partial discharge (PD) is a promising tool for condition monitoring of cables. This study presents the development of a modelling scheme for simulation of EM wave propagation due to PDs occurring at cavities in the cross-linked polyethylene (XLPE) insulation. Simulation models for generation and propagation of PD pulses through the cable geometry are developed. A finite element method (FEM)-based model is developed to simulate the PD current along cylindrical voids in the cable insulation. The initial fields due to the PD currents in the voids are simulated by an FEM model. The propagated EM fields inside and outside the cable surface due to PD's occurring inside the voids are analysed by a finite-difference time-domain model. The analysis of propagated EM fields due to a Gaussian-shaped PD pulse identifies the dominant components of propagated electric and magnetic fields along the cable geometry. Furthermore, the propagated EM fields at the cable sheath due to different void dimensions are simulated and experimental validation of the propagated magnetic field is conducted with measurement by an high-frequency current transformer sensor installed around an earth wire from the sheath of an 11 kV XLPE cable.

Subsea Cable Condition Monitoring With Distributed Optical Fiber Vibration Sensor

A novel subsea cable condition monitoring technique based on embedded optical fiber inside the cable is demonstrated. It is shown that a distributed optical fiber vibration sensor can be used to map dynamic strains all along the cable simultaneously. It is experimentally shown that such system can fully quantify the location and strain level at each point on the cable as a function of time for both abrupt impact and cyclical loading. The sensing system demonstrated a spatial and strain resolution of 1 m and $4\, \mu \varepsilon$ , respectively, over a 10-km sensing range.

Identification and Location of PD Defects in Medium voltage Underground Power Cables Using High Frequency Current Transformer

Fault location is an important diagnostic task in condition monitoring of underground medium voltage cables. Available solutions are well capable of determining the location of a single partial discharge (PD) defect on a cable section. In case several PD defects are active simultaneously along a cable section, the interpretation of the measured data becomes complex to identify the presence of more than one PD sources. In this paper, experimental investigation of two PD defects/sources at different locations on a medium voltage (MV) cable section is presented. A high frequency current transformer is used for single end PD measurements. Time domain reflectometry-based in-depth study of the reflected pulses provides the most valuable information to identify the presence of PD sources which further leads to the location of the individual PD sources. In this paper, the proposed solution is presented for two PD sources, however, the same methodology can be extended to locate multiple PD sources on the cable.

Numerical study of influential factors on partial discharges in HVDC XLPE cables

PurposeFor the dramatically developed high voltage direct current (HVDC) power transmission, HVDC cables play a vital role in the power transmission across seas and connections with renewable power sources. However, the condition monitoring of HVDC cables is still a challenging research topic. This paper aims to understand the influence of external factors, namely, current, cavity location and material properties, on partial discharge (PD) characteristics in HVDC cable in a numerical way referring to the refined Niemeyer’s model.Design/methodology/approachThe influences of the three external factors are studied by a proposed numerical model for DC PDs based on the modification of a conventional PD model for AC voltage via a finite element analysis method.FindingsThe external factors can influence the discharge magnitude and discharge repetition rate via affecting the electrical conductivity of the material: DC PD is more frequent and with higher discharge magnitude when the cavity is closer to the conductor or the current through the conductor is higher. Both DC PD repetition rate and average discharge magnitude in long-term aged material are lower than virgin material. The effect of discharge on insulation degradation becomes decreasingly significant.Research limitations/implicationsThe current work is based on the numerical modelling of DC PDs. Further experimental validations and comparisons are essential for improving the model.Practical implicationsThe studies of the influence factors for PDs under HVDC voltage can benefit the research and practical power transmission on DC PDs, contributing the design and test of DC PDs in HVDC cables, exploring the understandings of the DC PDs’ mechanism.Originality/valueThis paper, to the best of author’s knowledge, first studies the influence factors on DC PDs based on the numerical modelling work.

Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.

Review of Machine Vision Based Insulator Inspection Systems for Overhead Power Distribution System

The necessity to have reliable and quality power distribution is increasing, and hence there is great scope for research on automation of distribution system. There are signs of increased research in the work on condition monitoring of insulators during the last few decades. The possible failures can be predicted before they actually occur by using the condition monitoring of cables or any electrical equipment on-line. Those assets such as towers, conductors and insulators which are on the threshold of failure have to be replaced or repaired, so that forced outages reduce. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. In developed countries aerial patrolling has been adopted to monitor the insulators as an alternative. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. This review paper covers overall aspects of automatic detection of defects of insulator systems of electric power lines and classification into different classes by using vision-based techniques.

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world.

Development of indentation techniques in support of cable condition monitoring programs

This paper summarizes work performed over the past few years at Atomic Energy of Canada Limited (AECL) to further develop indentation techniques in support of cable condition monitoring programs. Sensitivity to cable degradation of the commonly used specific compressive stiffness (or indenter modulus) is assessed based on laboratory testing of unaged, thermally aged, and irradiated cable samples. Another technique, which consists of measuring recovery time during the post-indentation phase, is also assessed for a variety of tested jacket and insulation materials. The document includes a review of the effect of indenter design and testing parameters on indentation results and provides input towards potential revisions of the IEC/IEEE 62582-2 Standard.

Non-intrusive condition monitoring of electrical cables within the industrial sector to prevent cables failing prematurely

No abstract available

An Advanced Partial Discharge Recognition Strategy of Power Cable

Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently.

Condition Monitoring of Medium Voltage Electrical Cables by Means of Partial Discharge Measurements

The purpose of this paper is to discuss condition monitoring (CM) of medium voltage electrical cables by means of partial discharge (PD) measurements. Electrical cables are exposed to a variety of operational and environmental stressors. The stressors will lead to the degradation of the cable's insulation material and ultimately to cable failure. The premature failure of cables can cause blackouts and will have a significant effect on the safety of such a network. It is therefore crucial to constantly monitor the condition of electrical cables. The first part of this paper is focussed on fundamental theory concepts regarding CM of electrical cables as well as PD. The derivation of mathematical models for the simulation of PD is also discussed. The simulation of discharge activity is due to a single void within the insulation material of medium voltage cross-linked polyethylene (XLPE) cables. The simulations were performed in the MATLAB ® Simulink ® environment, in order to investigate the effects of a variety of parameters on the characteristics of the PD signal. A non- intrusive CM technique was designed for the detection of PD activity within cables. The CM technique was used to measure and analyse practical PD data. Two MATLAB ® programs were

On-line PD detection and localization in cross-bonded HV cable systems

This paper addresses the detection and localization of partial discharge (PD) in crossbonded (CB) high voltage (HV) cables. A great deal has been published in recent years on PD based cable insulation condition monitoring, diagnostics and localization in medium voltage (MV) and high voltage (HV) cables. The topic of pulse propagation and PD source localization in CB HV cable systems has yet to be significantly investigated. The main challenge to PD monitoring of CB HV cables is as a result of the interconnectedness of the sheaths of the three single phase cables. The cross-bonding of the sheaths makes it difficult to localize which of the three phases a PD signal has emanated from. Co-axial cables are used to connect cable sheaths to cable link boxes, for ease of installation and protection against moisture. A second challenge is, therefore, the coupling effect when a PD pulse propagates in HV cable joints and the co-axial cables, making PD detection and localization more complex. The paper presents experimental investigations into PD pulse coupling between the cable center conductor and the sheath and the behavior of PD pulse propagation in CB HV cables. It proposes a model to describe PD pulse propagation in a CB HV cable system to allow monitoring and localization, and also presents the knowledge rules required for PD localization in CB HV cable systems.

Parameter estimation technique for the semi-conducting layers in single-core XLPE cable

Partial discharge (PD) detection and location is of great significance for the power cable insulation condition monitoring, where the high-frequency (HF) model is needed to analyze PD propagation. Considering the influences of conductor skin effects and semi-conducting layers (SCL), this paper presents the equivalent Γ-section lumped parameter model for single-core cross-linked polyethylene (XLPE) insulation cables. The longitudinal parameters per-unit-length in the model are obtained using available geometric data by vector fitting method. Unfortunately, the dielectric characteristics of SCL in XLPE cables, especially for an installed one, are usually unknown and hard to measure. To address this issue, based on the simulated annealing algorithm, an optimization approach for estimating the transversal parameters in the Γ-circuit model is developed. The proposed technique is then carried out on the XLPE cable with simulated and real SCL, respectively. Furthermore, the estimation results of the real case are validated on a 603 m cable with kinds of HF pulses responses.

Simulation of Partial Discharge Propagation Characteristics in XLPE Cables and Joints

Accurate Partial discharge (PD) detection and location is of great significance for the power cable insulation condition monitoring. To achieve this, an adequate cable model to analyze pulse propagation is needed. Considering the complex structure of the cable, the equivalent circuit model is no longer applicable. In this paper a numerical electromagnetic method, FDTD, is applied. To analysis wave propagation characteristics in time domain, a well-established cable model is constructed. By simulating the electromagnetic (EM) field propagation inside the cable and the cable joint, the components of the electric fields excited by the PD source in different directions are calculated in order to evaluate the influence of the cable joint in practice, the calculated PD waveforms in the cable are compared with those of the cable joint model in FDTD.

The Design and Implementation of Cable Condition Monitoring System

This paper describes the detail of design and implementation of cable condition monitoring system, including accessing and sharing of monitoring data, visualization, query, analysis and device management.

Time Domain simulation of PD Propagation in XLPE Cables Considering Frequency Dependent Parameters

Partial discharge (PD) detection and location is of great significance for the power cable insulation condition monitoring, where analysis of the PD propagation is needed. This paper presents the single-core model of cross-linked polyethylene (XLPE) cable considering the influence of skin effect and semi-conducting screens. To analysis the propagation of PD pulse in time domain, the model firstly is rational approximated using vector fitting method. Based on the fitting results, the π section lumped parameters model is then proposed. Examples on two cables with different permittivity approximating forms of semiconducting screens are presented.

A smart grid needs smart monitoring

Over the past few decades, the electricity consumption has experienced phenomenal growth across the world. This may impose heavy load on power plants such as cables, as many of them are approaching the designed life consequently resulting in the reduction of reliability of power networks. The constraint imposed by aged power cables on increased customer demand on supply reliability has become a bottleneck in the operation of electricity networks. Therefore, smart monitoring plays a crucial role in improving the reliability of power network. This paper aims to discuss the importance of smart condition monitoring, taking power cable as an example, in smart grid. The paper introduces smart monitoring and asset management in smart grid in details and presents a physical configuration of on-line cable condition monitoring system. Partial discharge diagnosis is selected as an example in the paper to explain the benefits when smart monitoring is applied.

Diagnosis of MV Power Cables for Nuclear Power Plants

The diagnosis of MV (Medium Voltage) cable used in NPPs (Nuclear Power Plants) has become one of the most urgent issues. In accordance with PSR (Periodic Safety Review) results, continuous condition monitoring of cables is also continuously required. In this paper, HFPD (High Frequency Partial Discharge) measurement and an infrared imaging thermography test were performed to check the status of 13.8 and 4.16 kV MV cables installed in the domestic power plant. The diagnosis was found to be good for the target cable. The on-line diagnostic technology to assess the condition of MV cables during the operation of a power plant will contribute to enhance the safety of NPPs. In the future, these latest technologies will be applied to perform condition monitoring of HV (High Voltage) cables in NPPs.

Advanced Cable-Testing Techniques for Nuclear Power Plants

Cable condition monitoring involves a variety of testing or monitoring methods, none without limitations. Mechanical and chemical tests are only local in their effectiveness; they can miss problems in the untested cable. Electrical-cable-condition-monitoring tests—including insulation resistance tests, impedance measurements (such as the LCR test), and reflectometry or “cable radar” methods—make it possible to test entire cable circuits while they remain in operation. Impedance measurements enable the evaluation of cable condition factors such as dielectric absorption ratio, polarization index, quality factor, and dissipation factor. Several new electrical measurement methods, including time or frequency domain reflectometry, and a wireless microsensor technology called AgeAlert™, are showing promise as techniques for in situ monitoring of the nuclear power plant cable condition. The integration of all these methods and their combination with end-device testing methods represent a new application of cable condition monitoring that promises to provide the correlation between aging test results and the aging condition of in situ cables that individual methods by themselves do not provide.