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In this paper, the design and development of a bio-inspired UHF sensor for partial discharge detection in power transformers is presented. The UHF sensor was developed for external use in dielectric windows of power transformers. For this purpose, a microstrip antenna was designed with a radiating element shape based on the leaf of the Jatropha mollissima (Pohl) Baill plant. Then, an epoxy coating and an aluminium enclosure were developed to protect the antenna against corrosion and to provide mechanical support, external noise immunity, and a lifetime compatibility with power transformers. In order to verify the electrical parameters of the developed sensor, measurements of the gain and the reflection coefficient were performed in an anechoic chamber. Lastly, the antenna sensitivity for denominated partial discharge (PD) detection was compared with the IEC 60270 standard method. For this purpose, simultaneous tests were carried out in a partial discharge generator setup, composed of an oil cell with needle-plane electrodes. The experimental tests demonstrated the effectiveness of the sensor for detecting PD signals with apparent charge values higher than 35 pC.

This paper presents a comparison between the partial discharge (PD) characteristics of g[sup.3] and dry air for gas-insulated switchgears. PD signals were measured with a conventional method according to IEC 60270 and an ultra-high frequency (UHF) method. The partial discharge inception voltages (PDIVs) of g[sup.3] and dry air are about 74% to 84% and 58% to 72%, respectively, in the protrusion on conductor (POC) system, and 90% to 96% and 80% to 93%, respectively, in the free moving particle (FMP) system, depending on the gas pressure of 0.1 MPa to 0.5 MPa. The single PD pulse in time and frequency domains are not distinguished according to gas type. The PRPD patterns have different phase angles depending on the gas type in the POC, while the phase angle is 0–360° in the FMP, regardless of the gas type. Lastly, the correlation was analyzed, showing that the output voltage in mV of the UHF sensor increases linearly in accordance with the apparent discharge in pC, regardless of the gas type. The experimental results in this paper are important as a fundamental database for the application of UHF monitoring systems in an eco-friendly GIS.

Partial discharge (PD) measurements provide valuable information for assessing the condition of high voltage (HV) insulation systems, contributing to their quality assurance. Different PD measuring techniques have been developed in the last years specially designed to perform on-line measurements. Non-conventional PD methods operating in high frequency bands are usually used when this type of tests are carried out. In PD measurements the signal acquisition, the subsequent signal processing and the capability to obtain an accurate diagnosis are conditioned by the selection of a suitable detection technique and by the implementation of effective signal processing tools. This paper proposes an optimized electromagnetic detection method based on the combined use of wideband PD sensors for measurements performed in the HF and UHF frequency ranges, together with the implementation of powerful processing tools. The effectiveness of the measuring techniques proposed is demonstrated through an example, where several PD sources are measured simultaneously in a HV installation consisting of a cable system connected by a plug-in terminal to a gas insulated substation (GIS) compartment.

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.

The ultra-high-frequency (UHF) detection method is highly accurate and has a fault localization function. At present, most gas-insulated switchgear (GIS) installations are equipped with online UHF monitoring devices to detect partial discharges. In order to ensure the accuracy of the detection results, UHF sensors need to be verified regularly. UHF sensors used for online monitoring are usually installed at the handhole of the GIS and cannot be removed. Measuring the laboratory verification indexes (e.g., equivalent height, dynamic range, etc.) of the sensors directly is very difficult. However, it is easier to measure S11 of the sensor for verification and S21 between it and the neighboring sensors by injecting power signals. Accordingly, this paper proposes a degradation identification method for GIS UHF sensors using a cross-comparison of S-parameters. When sensor sensitivity decreases, S11 increases while S21 decreases, both serving as effective indicators of performance degradation. In this study, the equivalent S-parameter network and the variation mechanisms of S11 and S21 during sensor verification were first analyzed. Normal and typically degraded sensor models were then constructed and coupled in different GIS structures for electromagnetic simulation. The simulation and on-site verification results show that S11 is mainly affected by the sensor’s intrinsic performance and installation conditions at the inspection port, whereas S21 is predominantly influenced by sensor performance and the propagation characteristics of the GIS structure. Through cross-comparison of S11 and S21 at corresponding positions across three phases, sensor aging or failure can be effectively identified, enabling rapid on-site verification without removing the sensors. The proposed method was successfully validated on actual GIS equipment at the China Southern Power Grid Research Institute. It exhibits high accuracy, efficiency, and strong engineering applicability, enabling the early detection of degraded sensors and providing valuable support for condition assessment and maintenance decision-making in GIS online monitoring systems.

This paper presents a comparison between the partial discharge (PD) characteristics of g3 and dry air for gas-insulated switchgears. PD signals were measured with a conventional method according to IEC 60270 and an ultra-high frequency (UHF) method. The partial discharge inception voltages (PDIVs) of g3 and dry air are about 74% to 84% and 58% to 72%, respectively, in the protrusion on conductor (POC) system, and 90% to 96% and 80% to 93%, respectively, in the free moving particle (FMP) system, depending on the gas pressure of 0.1 MPa to 0.5 MPa. The single PD pulse in time and frequency domains are not distinguished according to gas type. The PRPD patterns have different phase angles depending on the gas type in the POC, while the phase angle is 0–360° in the FMP, regardless of the gas type. Lastly, the correlation was analyzed, showing that the output voltage in mV of the UHF sensor increases linearly in accordance with the apparent discharge in pC, regardless of the gas type. The experimental results in this paper are important as a fundamental database for the application of UHF monitoring systems in an eco-friendly GIS.

In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods.

Effective Partial Discharge (PD) localization can detect the insulation problems of the power equipment in a substation and improve the reliability of power systems. Typical Ultra-High Frequency (UHF) PD localization methods are mainly based on time difference information, which need a high sampling rate system. This paper proposes a novel PD localization method based on a received signal strength indicator (RSSI) fingerprint to quickly locate the power equipment with potential insulation defects. The proposed method consists of two stages. In the offline stage, the RSSI fingerprint data of the detection area is measured by a wireless UHF sensor array and processed by a clustering algorithm to reduce the PD interference and abnormal RSSI values. In the online stage, when PD happens, the RSSI fingerprint of PD is measured via the input of pattern recognition for PD localization. To achieve an accurate localization, the pattern recognition process is divided into two steps: a preliminary localization is implemented by cluster recognition to reduce the localization region, and the compressed sensing algorithm is used for accurate PD localization. A field test in a substation indicates that the mean localization error of the proposed method is 1.25 m, and 89.6% localization errors are less than 3 m.

UHF loop antenna sensors have been widely used for detecting partial discharges (PD) in high-voltage (HV) systems, their application in detecting surface discharges (SD) remains underexplored. Current technologies primarily focus on detecting bulk discharge events, leaving a significant gap in understanding the effectiveness of loop antennas for surface discharge detection. Surface discharges exhibit distinct electromagnetic characteristics that require optimized detection methods. This study investigates the use of loop antennas as Ultra High Frequency (UHF) sensors for detecting surface discharges on glass-type outdoor insulators. It delves into the design, development, and evaluation of the loop antenna, focusing on its performance in identifying dominant frequencies associated with SD events. Experimental results demonstrate the antenna's effectiveness at its resonant frequencies and its capability to detect SD over varying distances. These findings suggest that loop antennas are highly sensitive and cost-effective solutions for real-time monitoring of SD activities, improving maintenance strategies and increasing the reliability of high-voltage (HV) power transmission systems.

A well-defined condition-monitoring for power transformers is key to implementing a correct condition-based maintenance (CBM). In this regard, partial discharges (PD) measurement and its analysis allows to carry out on-line maintenance following the standards IEC-60270 and IEC-60076. However, new PD measurements techniques, such as acoustics or electromagnetic (EM) acquisitions using ultra-high-frequency (UHF) sensors are being taken into account, IEC-62478. PD measurements with antennas and the effect of their EM propagation in power transformer tanks is an open research topic that is considered in this paper. In this sense, an empty tank model is studied as a rectangular cavity and their resonances are calculated and compared with their measurement with a network analyser. Besides, two low cost improved monopole antennas deployed inside and outside of the tank model capture background noise and PD pulses in three different test objects (Nomex, twisted pair and insulator). The average spectrum of them are compared and can be found that mainly, the antenna frequency response, the frequency content distribution depending on the PD source and the enclosure resonances modes are the main factors to be considered in PD acquisitions with these sensors. Finally, with this set-up, it is possible to measure PD activity inside the tank from outside.