There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In recent years, many novel sensing nanomaterials were explored experimentally and theoretically as chemical gas sensors to detect sulphur hexafluoride (SF6) decomposed species with rapid response, high sensitivity and selectivity, as well as low cost, in order to estimate and guarantee the operation status of SF6 insulation devices. However, there has been no systemic review yet to comprehensively understand their sensing mechanism and conclude their sensing application. Such kinds of nanomaterials include traditional carbon-based materials such as carbon nanotube and graphene, metal oxides such as SnO2, ZnO and TiO2, transition metal dichalcogenides such as MoS2 monolayer and MoTe2 monolayer as well as graphene-like InN monolayer. A systemic review would be significant to give a profound insight into the exploration of nanomaterial-based chemical gas sensors in the field of electrical engineering and to help youth scholars first stepping in this field rapidly master the progress inside. Thus, the authors attempt to complete this review here. They are hopeful that this review could provide some guidance to fulfil such purpose in the near future, and it would be full of perspective to explore more novel nanomaterials as defect indicators in SF6 insulated devices. Furthermore, it is their hope that this review could broaden the sensing application of admirable nanomaterials in many fields such as environmental monitoring and pollutant scavenging.

Nanomaterials-based gas sensors of SF6 decomposed species for evaluating the operation status of high-voltage insulation devices

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.

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A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables

Power utilities are struggling to reduce power failure incidents in substations and their components to operate more reliably and economically [1]. Many power failures are produced directly or indirectly because of the insulation system of utility components [2], [3]. The selection of the insulation should ensure power plant operational continuity along with completely resolving or significantly limiting the actual power system's failures [4]. Gas insulated substations (GIS) have the best insulation performance which ensures achieving minimum failure incidents, although at high installation cost. The most common insulating gas used in GIS is Sulfur hexafluoride (SF 6 ) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium [5]. Basic GIS and gas insulated transmission lines (GITL or GIL) consist of a conductor supported by solid insulators inside an enclosure filled with SF 6 gas or its mixture [6].

Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

Condition monitoring of an operating apparatus is essential for lifespan assessment and maintenance planning in a power system. Electrical insulation is a critical aspect to be monitored, since it is susceptible to failure under high electrical stress. To avoid unexpected breakdowns, the level of partial discharge (PD) activity should be continuously monitored because PD occurrence can accelerate the aging process of insulation in high voltage equipment and result in catastrophic failure if the associated defects are not treated at an early stage. For on-site PD detection, the ultra-high frequency (UHF) method was employed in the field and showed its effectiveness as a detection technique. The main advantage of the UHF method is its immunity to external electromagnetic interference with a high signal-to-noise ratio, which is necessary for on-site monitoring. Considering the detection process, sensors play a critical role in capturing signals from PD sources and transmitting them onto the measurement system. In this paper, UHF sensors applied in PD detection were comprehensively reviewed. In particular, for power transformers, the effects of the physical structure on UHF signals and practical applications of UHF sensors including PD localization techniques were discussed. The aim of this review was to present state-of-the-art UHF sensors in PD detection and facilitate future improvements in the UHF method.

Application of UHF Sensors in Power System Equipment for Partial Discharge Detection: A Review

A novel gas insulated switchgear (GIS) partial discharge (PD) sensor with integrated optical fiber and ultrahigh frequency (UHF) method is developed in this paper. The integrated sensor is formed by placing an optical fiber on the surface of the UHF sensor. The novel sensor can be mounted inside the GIS and measure the PD through optical and UHF signals at the same time. The validity and sensitivity of the sensor are proved by experiments on a physical 110 kV GIS. This PD detection sensor solves the problem that the optical fiber is difficult to be mounted inside GIS and provides a novel method for PD detection in GIS.

A Novel GIS Partial Discharge Detection Sensor With Integrated Optical and UHF Methods

Detecting partial discharge (PD) in gas-insulated switchgear (GIS) is an effective way to diagnose the insulation state of GIS When PD occurs in GIS, optical and electromagnetic signals will be emitted from the discharge source PD can be detected by analyzing these signals At present, the ultrahigh-frequency (UHF) method is one of the most popular methods for detecting the PD in GIS However, the optical method has not been in the field due to the problem of mounting optical sensor into GIS, even though it has higher sensitivity and better anti-interference performance To settle this problem, a novel GIS PD detection sensor integrated with the optical fiber and UHF method is developed in this paper, which can fully utilize the advantages both of UHF and optical methods The simulation results indicate that the integrated sensor can be mounted into GIS on the premise of not affecting the electric field distribution in GIS In addition, the integrated sensor has a good UHF performance The PD signals of typical defects at several locations were measured in a physical GIS using the integrated sensor The results show that the integrated sensor can detect the optical and UHF signals induced by PD at the same time The optical method has higher sensitivity than the UHF method for the protrusion defect The amplitude and the number of the optical signals induced by PD clearly decrease with the increase in the detection distance The studies in this paper are helpful to improve the accuracy and sensitivity of PD detection in GIS on site

A Novel PD Detection Technique for Use in GIS Based on a Combination of UHF and Optical Sensors

A partial discharge (PD) detection method for SF6 insulated inverted current transformers (SIICTs) adopting an inner shield case as an ultra-high frequency (UHF) sensor is proposed in this paper. The inner shield case is a component of the SIICT. Its structure is similar to that of an antenna and can be used as a UHF sensor to measure the PD UHF signal. A 110 kV current transformer is used as an experimental object and the voltage standing-wave ratio of the inner shield case is calculated. The validity and sensitivity of this method are proved by experiments with ac voltage. This PD detection method solves the problem that the UHF sensor is difficult to mount inside current transformers and provides a novel method for PD detection of SIICTs.

A Partial Discharge Detection Method for SF 6 Insulated Inverted Current Transformers Adopting Inner Shield Case as UHF Sensor

The insulation problem of converter transformers has become one of the core issues in HVDC system, which is receiving more and more attention. A converter transformer has complex structures and requires high insulation level. The voltage waveform which the converter transformer insulation withstands is different from AC transformers. In the valve side of the converter transformer, the insulation endures a mixed voltage of AC voltage, DC voltage, harmonics and the pulsating component etc. Partial discharge (PD) occurring in oil-pressboard insulation is the one of the main causes of insulation breakdown, and it is the main factor of electrical aging of materials. Even though PD characteristics under pure AC or DC voltage waveform have been studied by many researchers, little research has been done with the mixed voltage. In this paper, the AC + DC mixed voltage experiment system is set up to study the PD characteristics under mixed voltage waveforms. The study results indicate that the pressboard withstands most of the DC component and void withstands most of the AC component when applied mixed voltage. For PD inception voltage, it is decided by both the AC component and DC component. For PD development, AC component has more significant influence than DC component. However, the DC component is found to promote PD activity under AC half cycle with the same DC polarity and inhibit PD activity under AC half cycle with the opposite DC polarity. The results in this paper provide an experimental foundation for the converter transformer insulation breakdown mechanism and analysis.

PD characteristics of oil-pressboard insulation under AC and DC mixed voltage

Gas insulated switchgear (GIS) is used in power system more and more widely. With the increased voltage level of power system, field impulse voltage withstand test becomes increasingly important. It would be beneficial for a comprehensive insulation evaluation to carry out the detection of partial discharge (PD) under impulse voltage withstand test in field. Metal protrusions or particles on HV conductor are typical defects in GIS and are critical at lightning impulse voltages. This paper therefore aims at PD characteristics study on needle-plane defect under oscillating lightning impulse (OLI). PD detection system was established based on ultra-high frequency (UHF) and pulse current detection. Then, the PD of a needle-plane defect under negative OLI was measured. The relation between statistical time lag, PD amplitude, PD times and the applied voltage was analyzed respectively based on UHF detection. The results show that UHF detection has high sensitivity and it is suitable in field impulse voltage withstand test. PDs occur around each wave peak of the applied voltage. With the increase of peak voltage, PD magnitude and PD times in each oscillation cycle both increase linearly. Further analysis indicates that the occurrence and development of PD under negative OLI can be interpreted by electron emission from metal surface, field detachment from negative ions and corona stabilization effect. This approach is valuable for PD measurement and analysis in field impulse voltage withstand test for GIS.

Xutao Han

Papers

A Novel GIS Partial Discharge Detection Sensor With Integrated Optical and UHF Methods

A Novel PD Detection Technique for Use in GIS Based on a Combination of UHF and Optical Sensors

A Partial Discharge Detection Method for SF 6 Insulated Inverted Current Transformers Adopting Inner Shield Case as UHF Sensor

PD characteristics of oil-pressboard insulation under AC and DC mixed voltage

Partial discharge detection and analysis of needle-plane defect in SF6 under negative oscillating lightning impulse voltage based on UHF method