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High voltage direct current (HVDC) cable is an important part in the electric power transmission and distribution systems. However, very little research has been carried out on partial discharge under direct current (DC) conditions. Niemeyer’s model has been widely used under alternating current (AC) conditions. This paper aims to intend to modify the Niemeyer’s model considering both electric field and charge dynamics under DC conditions, and therefore proposes a numerical model describing partial discharge characteristics in HVDC cable.

This paper intends to understand partial discharge characteristics under DC conditions through numerical modelling. Niemeyer’s model that has been widely used under AC conditions has been modified, taking both electric field and charge dynamics under DC conditions into consideration. The effects of loading level or current through the conductor, cavity location and material properties on partial discharges have also been studied.

Electrical conductivity is important in determining the characteristics of partial discharge under DC conditions and discharges tend to happen in short when the cavity field exceeds the inception level under the parameter values studied in the paper.

Building the numerical model is the purpose of the paper, and there is lack in experiment and the comparison between the simulation results and experiment.

The proposed model provides the numerical model describing partial discharge in HVDC cable and helps understand the partial discharge mechanism under DC voltage.

To the best of the author’s knowledge, this paper is a very early research on the numerical modelling work on partial discharge under DC voltage.

The paper proposes and presents a comprehensive and integrated circuit model for investigating the behaviour of partial discharges occurring in voids inside the solid insulations of medium and high voltage cables.

The model is based on the well‐known three capacitors model, which is remarkably improved to handle physical parameters such as cavity size, position, shape and pressure, environmental parameters such as cable temperature, in addition to operational parameters such as the contributions of the avalanche of free electrons inside the cavity through considering stochastic time delays.

A complete, flexible and reliable model for partial discharges in voids inside the solid insulation of medium and high voltage cables is presented whose output agrees with experimental reported results.

The proposed model deals only with single voids, and the semiconductor layers in the insulation of cables are not considered.

The model can be used in different physical, environmental and operational conditions in order to investigate the characteristics of partial discharge signals to be used as the bases for partial discharge detection and classification in power cables.

This paper presents a novel comprehensive and integrated circuit model with controlling functions to propose the behaviour of partial discharge occurring in voids inside the solid insulation of power cables. The model provides the contribution of geometrical parameters of the void, and operational conditions such as cable temperature and source frequency in partial discharge analysis.

For 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.

The 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.

The 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.

The current work is based on the numerical modelling of DC PDs. Further experimental validations and comparisons are essential for improving the model.

The 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.

This paper, to the best of author’s knowledge, first studies the influence factors on DC PDs based on the numerical modelling work.

In this paper a new computer‐aided digital system for partial discharge (PD) measurements is presented. The system is able to collect a great number of data, up to a pulse repetition rate of 100kHz, during the acquisition phase, thus allowing an off‐line analysis of PD data. After a description of the instrument, some experimental tests performed on epoxy specimens are here presented in terms of amplitude, phase and energy resolved histograms. Finally, emphasis is given to the very interesting use of the instrument for pattern recognition techniques in the discrimination and classification of discharges in HV components.

The purpose of this paper is to investigate the condition of insulation in high-voltage equipments using partial discharge (PD) measurements. It proposes the methods to eliminate several noises like white noise, random noise and discrete spectral interferences which severely pollutes the PD signals. The study aims to remove these noises from the PD signal effectively by preserving the signal features.

This paper employs fast Fourier transform, discrete wavelet transform and translational invariant wavelet transform (TIWT) for denoising the PD signals. The simulated damped exponential pulse and damped oscillatory pulse with low- and high-level noises and a measured PD signal are considered for this analysis. The conventional wavelet denoising approach is also improved by estimating the automated global optimum threshold value using genetic algorithm (GA). The statistical parameters are evaluated and compared. Among these methods, GA-based TIWT approach provides robustness and reduces computational complexity.

This paper provides effective condition monitoring of power apparatus using GA-based TIWT approach. This method provides the low value of mean square error, pulse amplitude distortion and also high reduction in noise level due to its robustness and reduced computational complexity. It suggests that this approach works well for both signals immersed in noise as well as for noise immersed in signals.

Because of the chosen PD signals, the research results may lack for multiple discharges. Therefore, researchers are encouraged to test the proposed propositions further.

The paper includes implication for the development of online testing for equipment analysis and diagnostics during normal operating condition. Corrective actions can be planned and implemented, resulting in reduced unscheduled downtime.

This PD-based analysis often present well in advance of insulation failure, asset managers can monitor it over time and make informed strategic decisions regarding the repair or replacement of the equipment. These predictive diagnostics help society to prioritize investments before an unexpected outage occurs.

This paper provides an enhanced study of condition monitoring of HV power apparatus by which life time of insulation can be increased by taking preventive measures.

Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN).

Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition.

The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN.

In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.

Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.

In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.

The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.

A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

The term “partial discharges” (PD) is a common term for various phenomena: discharges at points or edges of cylindrical conductors, in gases and gas insulated devices, liquid insulation materials, at borders between different insulation materials and, of course, in solid dielectrics. These phenomena result in insulation breakdowns, various disturbances to the environment, and after longer periods, some large‐scale failures. This paper presents the results of theoretical research of the behavior of a system of medium voltage covered conductors. This research work has been elaborated by the use of computer aided electric field calculations. For the confirmation of theoretical findings, practical measurements of partial discharges have been made.

Electrical characteristics of transformer oil (TO) have been studied during normal and thermal aging conditions. In this paper, breakdown voltage (BDV), partial discharge (PD), heat transfer results and the physical mechanisms considering the impact of varying the diameter of Al₂O₃ nanoparticles (NPs) have been investigated. Different quantities of the two sizes of Al₂O₃ were added to the oil using a two-step method to determine the positive effect of NPs on the electrical and thermal properties of TO. Finally, the physical mechanisms related to the obtained experimental results have been performed.

The implementation of nanoparticles in this paper was provided by US Research Nanomaterials, Inc., USA. The provided Al₂O₃ NPs have an average particle size of 20–80 nm and a specific surface area of 138 and 58 m²/g, respectively, which have a purity of over 99%. Thermal aging has been done. The IEC 60156 standard has been implemented to calculate the BDV, and a 500-mL volume test cell (Apar TO 1020) has been used. PD test is performed according to Standard IEC 60343, and a JDEVS-PDMA 300 device was used for this test.

BDV tests indicate that 20 nm Al₂O₃ is more effective at improving BDV than 80 nm Al₂O₃, with an improvement of 113% compared to 99% for the latter. The analysis of Weibull probability at BDV indicates that 20 nm Al₂O₃ performs better, with improvements of 141%, 125% and 112% at probabilities of 1, 10 and 50%, respectively. The results of the PD tests using the PDPR pattern also show that 20 nm Al₂O₃ is superior. For the heat transfer test, 0.05 g/L of both diameters were used to ensure fair conditions, and again, the advantage was with 20 nm Al₂O₃ (23% vs 18%).

The effect of Al₂O₃ NP diameter (20 and 80 nm) on various properties of virgin and aged TO has been investigated experimentally in this paper to examine the effect of proposed NP on electrical improvement of TO.

The purpose of this study is to provide an in-depth understanding about the indoor-orbital electrical inspection robot, which is useful for motivating the further investigation on the inspection of electrical equipment. Currently, electric energy has a strong correlation with the economic development of the country. Intelligent substations play an important role in the transmission and distribution of the electricity; the maintenance of the substation has attracted intensive attention due to the requirement of reliability and safety. The indoor-orbital electrical inspection robot has increasingly become the main tool to realize the unmanned. Hence, a systematic review is conducted systematically reviewing the current technical status of the indoor-orbital electrical inspection robot and discuss the existed problems.

In this paper, the most essential achievements in the field of indoor-orbital electrical inspection robots were reported to present the current status, and the mechanical structures and key inspective technologies were also discussed.

Four recommendations are provided from the analyzed review, which have made constructive comments on the overall structural design, functionality, intelligence and future development direction of the indoor-orbital electrical inspection robot, respectively.

To the best of the authors’ knowledge, this is the first systematic review study on indoor-orbital electrical inspection robots; it fills the theoretical gap and proffers design ideas and directions for the development of the indoor-orbital electrical inspection robot.