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Many power utilities may have problems with the quality of data in records about which feeder a distribution transformer is connected to. This affects the operation and maintenance of smart grid infrastructure, outage management, line loss management, and workforce safety. The traditional manual way of verifying and updating the distribution transformer connectivity (DTC) is time-consuming and labor-intensive. Researchers have proposed the method which makes use of secondary side single-phase voltage of distribution transformers to verify DTC. However, when the windings of distribution transformers are yyn0 connected, the performance of the single-phase voltage method (SPVM) is unsatisfactory due to the unbalanced three-phase voltages. This paper proposed a dimension reduction method (DRM) that can convert the unbalanced three-phase voltage to a balanced voltage. The data which include 29 days' secondary side voltages of 3866 distribution transformers have been collected. The performances of the DRM and the SPVM have been compared. Results show that the DRM always has higher accuracy compared with SPVM. The influences of the maximum voltage difference and the reference distribution transformer have also been discussed. The DRM's results are more stable compared with SPVM.

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications.

This two-part paper proposes a revised method for calculating high voltage (HV) cable sheath currents, and for fault location of cross-bonded HV cable systems. The equivalent circuit and the cable impedance per unit length under short-circuit fault conditions are revised from those under non-fault conditions. Part 1 of this paper proposes a revised phasor domain mathematical model for the sheath current calculation under fault conditions, following demonstration that short-circuit faults can cause changes in the equivalent circuit of the cable metal sheath, and cause changes in the cable impedance per unit length. The calculation results, as given in a case study, show that there exists a unique relation between the fault position and the sheath fault currents, making it possible to locate a fault by analyzing the characteristics of the sheath currents during a fault. Part 2 of the paper focuses on the fault location method via sheath current monitoring.

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. Firstly, 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, 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 (BPNN) and Support Vector Machine (SVM). 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.

This paper presents a case study on a 275 kV oil-filled cable. The condition assessment and diagnosis are based on analysis of cable surface temperature in relation to its current load and insulation dielectric loss. The work was initiated by a local abnormal temperature rise of 5.2 °C in cable surface temperature, which was observed during a routine inspection. The temperature rise occurred at bend area with a length of approximately one metre in the Blue phase. No PD activity was identified using on-line PD measurement. The relation between cable surface temperature, cable core temperature and cable insulation condition was then simulated based on the thermal model of power cables. According to simulation analysis, poor condition of cable insulation or oil from an oil duct penetrating a region under the cable surface were identified as possible reasons for the problem observed. An in service X-ray scanning technique was employed for further investigation and to aid diagnosis. The X-ray images revealed a slight distortion of the PVC sheath and the presence of multiple voids between cable insulation paper and the lead sheath. It was concluded that an oil leakage from the oil duct to the voids under the cable lead sheath was responsible for the local cable surface temperature rise. The result removed the concern of incipient cable breakdown, and a potential unplanned outage.

This paper presents a probabilistic dynamic programming algorithm to obtain the optimal cost-effective maintenance policy for a power cable. The algorithm determines the states which a cable might visit in the future and solves the functional equations of probabilistic dynamic programming by backward induction process. The optimisation model considers the probabilistic nature of cables failures. This work specifies the data needs, and presents a procedure to utilize maintenance data, failure data, cost data, and condition monitoring or diagnostic test data. The model can be used by power utility managers and regulators to assess the financial risk and schedule maintenance.

Regular maintenance inspection and testing is essential in extending cable life and reducing failure probability. This can be achieved by improving the conduit conditions and taking corrective actions on faulty cable components and accessories. Regulators and corporate governance among power utilities require the implementation of risk-based approaches to asset management. However, practitioners lack sufficient historical event data and knowledge that allow them to determine the failure probability of individual cable components, which is an essential component for risk assessment, due to that the high voltage (HV) cable population are relatively young, and many have not yet reached the end of their design life. This paper presents a novel holistic approach to allow the risk based maintenance strategy to be conveniently implemented for the cable conduit, cable terminations, joints, main bodies and the earthing systems separately for each cable circuit. Contributions include: (i) a failure frequency model which accounts for every past failure record of individual cable circuit components to approximate the probability of failure. This, when multiplying with the cable importance or failure consequence, yields the risk level of an individual cable component or a cable circuit; and (ii) a method of optimally scheduling the maintenance activities by setting the objective functions as the minimal cable system risk. The benefit of the simple failure frequency model has the advantage of not having to depend on human intervention and it does not need a large sample to generate valid results, as is the case with other statistical methods. Results of applying the proposed maintenance scheduling model to 21 selected High Voltage (HV) cable circuits show that the average risk can be significantly reduced while continuing with the same number of inspections and test operations.

The physics- based electro-thermal ageing model has been widely used for analysis of cable failures and for evaluation of insulation status for single cable circuits. However, previously published approaches only reflect increased failure probability on an annual basis which cannot be used to explain the phenomenon that higher number of cable failures have been observed during summer time by Scottish Power Energy Networks (SPEN). Despite the insulation degradation accumulated annually, higher daily temperature during summer seasons resulted in extra stochastic stress applied on cable insulation and led to increasing cable failure probability. During summer seasons, the majority of cable failures occurred when daily temperature was higher than 10 °C. This paper aims to improve the electro-thermal model to evaluate remnant insulation strength and failure probability with consideration of stochastic stress due to higher daily temperature. Failure occurs when the stochastic stress overwhelms the remnant insulation strength. With the improved model, failure probability can reflect higher number of failures during the summer season.

A delay-time concept based approach to scheduling of HV cable inspection and maintenance is proposed in this paper. Based on the delay-time concept, the progress of cable failure can be regarded as a two-stage process, one being a stochastic process of defects arrival which can be introduced by Poisson distribution and the other being a delay-time interval from defect to failure which can be expressed by exponential distribution. According to the service duration of a cable and its historical periodicity of inspection, the use of a statistical model representing cable failures for scheduling maintenance activities is established. Within the delay-time interval, a defect can be identified and eliminated prior to it turning into failure through daily inspection, testing and maintenance. Cable circuits within different service age are considered individually due to their characteristics of failure progress versus service age vary with individuals. Then the maintenance activities can be optimally scheduled by setting the objective functions as the minimal maintenance cost, where the overall cost of HV cable maintenance is considered as including the potential failure loss and daily inspection and testing cost. In this way, maintenance Scheduling can be automatically formulated and updated whenever failure data changes. The optimal approach have been applied to test against thousands of cable circuits in southern China. The economic expenditure could be reduced by 37.46%, compared with the total cost before optimization, because the periodicity of inspection has been optimized to fit within the delay-time interval from defect to failure.

Infrared thermography has been widely used in timely detection of overheating defects in cable accessories. However, the traditional manual diagnosis method is time-consuming and laborious, and it relies too much on expert experience. An autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed in this paper. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in images (cable terminations and grounding boxes are included in this paper). Then, the Mean-Shift algorithm is used for image segmentation to rapidly and accurately extract the area of overheating. This is achieved via comparing key regions of the three phase accessories. Next the temperature related characteristic parameters of the overheating region are calculated, and the diagnosis results are obtained in accordance with the relevant cable condition assessment criteria. The proposed method has been applied to test against actual infrared images, and results show that the cable accessories and their overheating regions can be located at different shooting angles and under various background conditions. The research helps reduce the dependence on human efforts and expertise and contributes to improving the practice of condition monitoring.

The Weibull model as a statistical method has been widely used for analysis of cable failure data and for prediction of future failure rate on an annual basis. However, previously published approaches cannot be relied on to model the phenomenon that higher number of failures of cable joint has been observed during summer time by Scottish Power Energy Networks (SPEN). Seasonal temperature higher than $20^{\circ}\mathrm{C}$ resulted in increasing failure rate of cable joints, especially for the joints which connect PILC cables. When the ambient temperature was lower than $15^{\circ}\mathrm{C}$, the failure rate of cable joint became smaller. This paper aims to improve the standard Weibull model to allow predictions of number of failures with consideration of seasonal temperature. The thermal stress caused due to seasonal temperature is treated as a variable to correct the failure rate obtained via standard Weibull analysis, as the number of failures has been heavily related to seasonal temperature. With the improved model, failure prediction can forecast higher number of failures during the summer season.

The associated daily maintenance effort and cost has been increasing with the growing use of high-voltage (HV) cable circuits. This study proposes a time-delay concept-based approach to maintenance scheduling of individual HV cable components, to allow periodicity of inspection to be optimised with consideration of time-delay interval between the instant of defect inception and final failure. Here, the progress from a defect to its resultant final failure can be represented as a two-stage process, one being a stochastic process of defect arrival and the other being a delay-time interval from defect to failure. Then an optimisation model is established to schedule maintenance activities based on the two statistical processes, where the objective function is set as the minimal maintenance cost. Cable circuits are divided into five components in accordance with the objects of inspection and testing. The components within different service ages are considered individually in the statistical models, and their accumulate failure numbers during each service year are used to automatically formulate and update periodicity of inspection. To illustrate the feasibility of the approach proposed, theoretical analysis on thousands of cable circuits are carried out. Results show that the economic expenditure could be reduced by 22.2% after optimisation.

Existing cable insulation condition assessment technologies, such as Dielectric Loss (tanδ) measurement and Partial Discharge (PD) Monitoring can only reflect the integral condition of, or local defects in, a cable circuit. From late 1990’s, the cable impedance spectroscopy (CIS) method, a measure of the magnitude and phase angle of impedance over a range of frequencies, has been explored for cable condition assessment and fault detection. The paper aims to build on previous research and present an in-depth study of the CIS method for use in condition assessment of total insulation and to investigate its ability to detect and locate the positions of local defects. Simulation results indicated that the objectives can be achieved via CIS. General insulation aging and local defects both cause decreases in the amplitude of cable impedance and increase or shift in the phase of cable impedance. General aging of insulation and the existence of local defects can be distinguished via the rate of decline of the magnitude of CIS. By comparing the integral operations on the CIS results of a healthy cable and the ones having a insulation of general aging and with defects at a series of pre-set locations, criteria are established to locate local defects.

Cross-bonded three-phase single-core Cross-linked Polyethylene (XLPE) cables are widely used in power transmission and distribution systems. A great deal has been published on on-line monitoring of HV cable systems to prevent failure. Relative Dielectric Losses (DL) monitoring reveals the general condition of the cable insulation. Generally, DL measurement relies on measuring operating voltage and insulation leakage current. The main challenge to on-line monitoring of DL in Cross-bonded High voltage (CB HV) cables is the interconnectedness of metal sheath. Measured sheath currents are a combination of the leakage currents of each section and the imbalanced circulating current. Differentiating the leakage current of each interconnected section from the measured data is complex. The second challenge is that co-axial cables are used to connect cable sheaths to cable link boxes, for ease of installation and protection against moisture. As a result, any measured value contains currents from the two sheaths connecting at a joint. The third challenge is that it is hard to obtain the reference voltage synchronously and with high accuracy. Consequently, this paper investigates separation of leakage current components and analysis of DL based on leakage currents without reference voltage. A circuit model is proposed to describe the leakage current separation method for CB HV cables to allow on-line monitoring of DL, and the criterion required for assessing relative change of DL among different phases or sections of the cable for insulation deterioration are presented. Operational measurements from a cable system are analyzed and discussed. Results show that there is no significant deterioration in this monitored major section of a CB HV cable system.

Cross-bonded metal sheath connection is applied in sectioned single-core power cables to reduce or eliminate the voltages that are induced in the sheath over long distances. However, cross-bonded cables present an opportunity as well as a challenge for online measurement and diagnosis of cable conditions. In this paper, a methodology to identify cable sheath faults through analysis of the sheath system currents in a cross-bonded cable system is presented. First, a numerical model is established to simulate the sheath currents in cross-bonded cable systems. Second, analyses of several faults, which happen frequently with serious consequences, are presented on the basis of current measurement at the link cable. Simulations of normal and fault conditions are given to determine the feasibility of fault diagnosis. A case study using field data from a cable tunnel in China considering the normal condition is presented to verify the numerical model. Results in normal condition show good consistency with field data with error less than 5%. Simulation results of fault conditions show that analysis of readings from six current sensors can distinguish different fault types and fault positions using the method proposed. Based on the analyses, criteria are established for sheath loop fault type diagnosis.

Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.

The study of failure behavior of a diverse population of cables is challenging. Previous attempts have failed to capture the complexity of cable system failures due to an independent analysis of multiple failure causes or influential factors. In this paper, the multiple correspondence analysis (MCA) is proposed for simultaneous analyses of multiple variables responsible for the cable failures and classification of cables into homogeneous groups in terms of past failure behavior. The proposed classification method is less subjective as it gives equal consideration to all the cable features. The methodology has been applied to the main cable section and cable joint failure data of a diverse population of cables obtained from a Chinese utility company. The failure data have six categorical variables related to cable features and failure characteristics. The application of MCA provided an enriched view and understanding of failure behavior by allowing visual exploration of the failure patterns and associations. Based on the past failure, the cable sections and joints were classified into three and four groups, respectively. The failure trend of each classified group is evaluated separately. Results show that failure history and trend of each classified group is different. Thus, they must be analyzed and treated differently in the forecasting or maintenance planning procedures.

Dielectric loss has long been recognized as one of the most important indicators of cable insulation health. Although huge efforts have been made in the past to measure the dielectric loss of cable circuits, there has been no report of any successful on-line techniques for the purpose. This paper proposes a new on-line insulation dielectric loss monitoring method, based on separation of currents collected from the co-axial cables connecting the cable sheathes and the cable link boxes. The principle and theoretical foundation of the method are demonstrated. MATLAB simulations, based on the real cable parameters in a 110 kV cable tunnel in China, indicate the error of the proposed method is less than 1 × 10 −3 %. The criteria for determining the relative dielectric loss in cable segments, based on the leakage current separation results, are demonstrated at the end. Initial results and analysis of implementation of the proposed method in the real world 110 kV cable tunnel are presented.

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.

The fast growing wind industry requires a more sophisticated fault detection approach in pitch-regulated wind turbine generators (WTG), particularly in the pitch system that has led to the highest failure frequency and downtime. Improved analysis of data from Supervisory Control and Data Acquisition (SCADA) systems can be used to generate alarms and signals that could provide earlier indication of WTG faults and allow operators to more effectively plan Operation and Maintenance (O&M) strategies prior to WTG failures. Several data-mining approaches, e.g. Artificial Neural Network (ANN), and Normal Behaviour Models (NBM) have been used for that purpose. However, practical applications are limited because of the SCADA data complexity and the lack of accuracy due to the use of SCADA data averaged over a period of 10 min for ANN training. This paper aims to propose a new pitch fault detection procedure using performance curve (PC) based NBMs. An advantage of the proposed approach is that the system consisting of NBMs and criteria, can be developed using technical specifications of studied WTGs. A second advantage is that training data is unnecessary prior to application of the system. In order to construct the proposed system, details of WTG operational states and PCs are studied. Power-generator speed (P-N) and pitch angle-generator speed (PA-N) curves are selected to set up NBMs due to the better fit between the measured data and theoretical PCs. Six case studies have been carried out to show the prognosis of WTG fault and to demonstrate the feasibility of the proposed method. The results illustrate that polluted slip rings and the pitch controller malfunctions could be detected by the proposed method 20 h and 13 h earlier than by the AI approaches investigated and the existing alarm system. In addition, the proposed approach is able to explain and visualize abnormal behaviour of WTGs during the fault conditions.

All component cells of the embryo-sac before and after fertilization in rice were isolated by manual microdissection under conditions either free of enzymes or combined with a short pulse of enzymatic treatment.In general, the frequency of isolated unfertilized or fertilized egg cells or central cells reached 15-40%. Various component cells of the embryo-sac after isolation were distinguished by their own morphological characteristics. The isolated cells were cultured in a microchamber and fed with dividing rice suspension cells. Both unfertilized and fertilized egg cells and central cells were induced to divide. Among them only the fertilized egg cells (the zygotes) developed into proembryo-like multicellular structures. The frequency of the first zygotic division and the frequency of multicellular structures were higher using the non-enzymatic method than using the enzymatic one.

Domestic electricity load profile is essential for energy planning and renewable energy system design. This paper presents analysis of domestic electric load characteristics and a method to model domestic and regional load profile. Multiple Gaussian functions are used to express the load characteristics in the proposed model. This is done by associating the Gaussian function parameters with the peak load changes, e.g. changing height parameters to reflect the peak magnitude. The result of the load curve represented with multiple Gaussian functions allows the model to generate a regional load profile using the number of homes, the number of bedrooms (Nr) and the number of occupants (Np). The proposed model simulates domestic load profile by its load demand change characteristics instead of its appliance ownership and use pattern, etc. Data requirement for the proposed method is significantly lower than the previous top-down and bottom-up approaches. Seasonal change is not included in the present paper, but the method is capable of including seasonal changes if each season’s load demand changes in relation to Np and Nr is available. A demonstration of modelling England and Wales’s national hourly load profile in 2001 and 2011 is presented in this paper. Comparison is made of the proposed method with two other published domestic load profile models. Results show that the proposed method improves the mean percentage errors by at least 5.7% on average hourly load profile.

Contaminant is the main factor affecting the insulation performance. The contaminant composition determines the inception voltage and the intensity of insulation discharge. This paper studies the intense discharge activity at low humidity (77% RH) in AC 500 kV Gangcheng substation where the contaminant contained glucose, and this phenomenon was rarely explained in previous publications. Electrical and chemical performances of glucose as a contaminant on insulation surface were investigated by means of discharge test (below 80% RH), artificial pollution test (100% RH), molecular dynamics simulations, and quantum chemistry calculations. The experimental results show that due to stronger moisture absorption capacity compared to non-glucose contaminant, glucose contamination sample has lower discharge inception voltage and stronger discharge intensity at 75% RH as the content of glucose exceeds 0.10 mg/cm2. Quantum chemical calculations reveal that the glucose molecules can decompose to generate free radicals with the unpaired electrons under the strong electric fields. The surface carbonation enhances the electron transfer rate and strengthens the discharge intensity. Moreover, the competitive adsorption of glucose and water molecules cause the low moisture discharge phenomenon, and it needs to be considered for insulation design and maintenance in areas producing sugar.

This paper compares the application of the Weibull distribution and the Crow-AMSAA (C-A) model to the analysis of cable joint failures. The procedures on how to use the two models to analyze failure data and to predict the future number of failures have been described before the models are applied to a set of early failure data. The data, which include 16 failures and 1126 suspensions, were collected from a regional power-supply company in China. This paper proves that the Weibull distribution provides more reliable results in the analysis of early-failure data since it considers the time to failure of each event. Whilst the method is more straightforward and requires less information, applying the C-A model can yield confusing results if not handled carefully. When analyzing the events, using time as x-axis and using the cable joint population as an axis can complement each other. Separating those data into subsections and analyzing them independently can yield useful information. Recent failure data can better reflect the current state of cable joints. The results of this paper should help utility asset managers better analyze their past failure events.

Cables are one of the main assets in the power grid particularly for distribution network where the loads are widely spaced, this is a vital issue in oil and gas power grid. Monitoring cables on-line is an impossible task due to the cost and the excessive number of distributed cables. In most of oil and gas production countries the ambient temperature hits 56 degree Celsius that adds to the failures of power cables and shorten the cables lifecycle and the hour to hour capacity.This paper presents the results of estimating the maximum operating temperature of the cable during different operating load and under different ambient temperature, it is anticipating its impacts on the cables lifecycle. This is compared to the cable manufacturer design specification associated with the normal operating temperature. The results identify the factors contribute significantly to the degradation of the physical insulation property of the cables. In this paper the response time in the thermal transient model is used to identify the remaining useful life (RUL) of the cable. The model is used as a diagnostic procedure to detect the degree of aging, the results show that RUL of underground cables is 7.5 years and for overhead cables is 13.3 years under ambient temperature of 47° C and maximum load current.

Cable condition monitoring techniques play a key role in informing operators of the state of cable assets. Instead of using traditional methods such as partial discharge and dielectric loss measurements, this paper presents a method for cable condition assessment via the measurement and analysis of cable surface temperature. First, the theory underpinning the calculation of cable surface temperature is introduced. Then the cable surface temperature rise in relation to cable dielectric loss is presented. The case of a 275kV oil-filled cable where a local surface temperature rise of $5^{o}{C}$ was observed is also investigated in the paper. Results indicate two reasons possibly responsible for the temperature rise. Finally, the calculations of cable temperature in relation to insulation degradation for 11kV cables show that diagnosis of MV PILC and EPR cables relying on cable surface temperature rise is possible.

With the increase in the utilization rate of EV (Electric vehicle) charging facilities, the perception of operation status and the intelligent maintenance have become the key technologies of charging station development. A charging station data analysis method based on the two-dimension data framework is proposed according to the power transmission characteristic and data classification. Combined with the frequency and characteristic of fault events as well as the working principle of the charging station, the hardware and software framework based on the data processing and analysis for state evaluation and scientific maintenance planning is designed. Finally, the actual operation and maintenance statistics of the electric bus charging station in Suzhou, China is taken as an example for analysis.

Electric Vehicles (EVs) are considered to be one of the most promising advancements to decarbonise the road transport. However, the realisation and sustainability of this technology demand the availability of public electrical chargers and fast charging techniques to substantially reduce the long charging time of conventional charging techniques. The work in this research presents an effort to determine the optimum Charge rate (C-rate), for a Li-ion cell, as a function of the State of Charge (SoC), in a 30 minutes charging time and, a Cut off Voltage (CoV) of 4.1 V. Firstly, a two-level modelling method is used to analyse the effect of charging performance of three different C-rates as a function of SoC with a typical CoV of 4.2 V. Secondly, the study is furthered by applying a period of 30 minutes charging time and a CoV of 4.1 V as the two constraints to obtain the boundary condition for the SoC. Thirdly, an integrated modelling approach deploying COMSOL LiveLink for Matlab toolbox is proposed to determine the optimum C-rate as a function of the SoC. The results show a 14 % rise in the SoC, at the determined optimum C-rate than that possible at a typical 1 C-rate.

Overtemperature due to accumulation of heat in a crowded cable trench accelerates the aging of cable insulation materials, and leads to shorter cable life. In order to assist maintenance engineers with additional information when increase in circuit ampacity demand requires installation of additional cables, an optimization method is proposed to reallocate the current of selected cable circuits using spare cable pipes in a cable trench. The objective is set in this paper as to limit the operating temperature and extend average service life of cables. Based on the Finite Element method, the temperature field in an underground cable trench which has 24 cable pipes, of which 20 have been occupied with operational cables, is simulated. According to previously reported cable aging theory, the stochastic electro-thermal degradation accumulation in cables is modeled, and the average service life of cables in the trench is assessed. An optimization method of reallocating the current by adding cables in spare pipes is established. It is found from results of cable average service life assessment that, by adopting the proposed optimization method, the average service life of the cables in the trench can be increased by 2.2 years while the maximum conductor core temperature among cables in the trench is reduced by 1.9 °C, comparing with the redistribution which was adopted without applying the proposed method.

In this paper, the broadband dielectric spectroscopy (BDS) method is applied to different service aged, 110 kV cables samples, to obtain their permittivity and tan delta under a frequency range from 0.1Hz to 107Hz. The service time of the service aged samples vary from 1 to 27 years. In particular, the permittivity and tan delta under 0.1 Hz and 50 Hz were analyzed to support the field test and on-site diagnostics. The results of service aged XLPE slices show that tan delta and the imaginary part of relative permittivity increased obviously after 15 years operation and the trend is more obvious as the duration of service increases, whilst the real part experienced little change. In order to apply this study to condition assessment of in-service cables, a set of revised criteria is proposed based on analysis of 0.1 Hz tan delta and IEEE standard 400.2 recommended values.

This paper presents a novel Partial Discharge (PD) localisation method, based on PD pulse Rise-Time and Transfer Function (RTTF), for use in on-line cable PD monitoring systems. This is based on the change in rise time as a function of cable length along which a PD pulse has propagated. A comparison with four other well-known location techniques, i.e. Time-Domain Reflectometry (TDR), Phase difference, Arrival Time Analysis (ATA) and Amplitude-Frequency (AF) mapping has been made and presented. Analysis shows that the TDR and Phase difference methods, although requiring a single measurement point, are only applicable to short length cable systems as both incident pulse and reflected pulse should be observable when it reaches the detection point. On the other hand, the ATA and AF mapping, which can be applied to longer cable systems, need to install detectors at multiple points and detect synchronously. In comparison, PD localisation by the proposed RTTF method, which requires detection of the incident pulse at only one measurement point, can be used to localise a PD source which is further away from the PD detection point than other methods and can be used in on-line PD monitoring systems. As on-line PD monitoring sensors commonly comprise of a high frequency current transformer (HFCT) and a high-pass filter, the rise time of detected PD pulses depends on the attenuation of the cable, the HFCT used and the filter applied. Simulations of pulse propagation in a cable and PD monitoring system are performed, based on analyses in the frequency domain using the concept of transfer functions. Results of RTTF from the simulations have been verified by laboratory experiments and on-site PD measurements, which provide evidence that the RTTF method is a very valuable and useful PD localisation technique.

Due to the electromagnetic induction between transmission lines and the associated grounding wires, power frequency current causes flow into the ground network via the metallic tower, which may interfere with grounding resistance measurements. This paper analyzes the mechanisms of this interference, and shows that power frequency interference becomes stronger at phase-wire transposition towers. Grounding resistance measurement by the variable frequency (VF) method has been proven to be effective in dealing with power frequency interference. However, the test equipment based on the VF method is always deactivated when the external interference voltage exceeds a preset level in order to avoid false readings. This paper aims at tackling the problem by introducing a reverse phase cancellation (RPC) method, which acquires and processes the interference signal before generating a cancelling signal with the same amplitude and frequency and a phase which is exactly opposite in polarity. This paper proves that the RPC method represents an excellent improvement over the VF method since it retains the advantages of VF and avoids the trip-out of test equipment due to excessive interference.

This paper develops a methodology to determine the impacts of high penetration level of fully electric vehicles (FEVs) charging loads on the thermal ageing of power distribution transformers. The method proposed in this paper is stochastically formulated by modelling the transformer life consumption due to FEVs charging loads as a function of ambient temperature, start time of FEVs charging, initial state-of-charge and charging modes. FEVs loads are modelled using the results from an analytical solution that predicts a cluster of FEVs chargers. A UK generic LV distribution network model and real load demand data are used to simulate FEVs’ impacts on the thermal ageing of LV power distribution transformers. Results show that the ambient temperature, FEVs penetration level, and start time of charging are the main factors that affect the transformer life expectancy. It was concluded that the smart charging scenario generally shows the best outcome from the loss of life reduction perspective. Meanwhile, public charging which shifts a large percentage of charging load to commercial and industrial areas can significantly alleviate the residential transformer loading thus has little impact on the loss of life of transformers. The proposed method in this paper can be easily applied to the determination of the optimum charging time as a function of existing loads, and ambient temperature.