Polar coordinate pattern approach to partial discharge on floating electrode defect in GIS

Widely used method for PD (partial discharge) measurement and analysis of existing underground power transmission cables is to store the detected signal pulses as PRPD (Phase Resolved Partial Discharge) data and to analyze the PD defect factors by the pattern type. Although the PRPD pattern data of various facility defects are acquired through the underground power transmission cable diagnosis, due to the PRPD data storage structure, the pulse generation information is destroyed over time, and reconversion into 3D PD pattern (PRPS: Phase Resolved Pulse Sequence) is impossible. Also recently, as a method for verifying the reliability of the power utility prevention diagnosis system, a simulated partial discharge is generated for each facility defect, and the diagnostic system verification that is capable of simultaneously measuring pulses and PD patterns using various sensors is performed, and it is necessary to develop a PD pulse generator based on simulated PRPS data in order to generate the pulses based on PRPS pattern using the existing PRPD pattern, and input the pulses into the system to perform the defect judgment performance test. To solve this problem, this study developed and verified a PD pattern generator that can generate pulses based on PRPS data, and an algorithm that converts existing PRPD pattern data currently owned by Korea Electric Power Corporation (KEPCO) to PRPS pattern data.

The task of detecting and classifying Partial Discharge (PD) in switchgear remains challenging due to the presence of inherent noise and the subtle differences among various PD signals under different operational conditions. Traditional PD detection methods have primarily relied either on analyzing time-series waveforms or on employing Phase-Resolved Partial Discharge (PRPD) patterns, which utilize extensive statistical information for classification. However, these approaches are notably susceptible to variations in hardware configurations and signal-to-noise ratios (SNR), limiting their effectiveness in real-world applications. This paper proposes a novel approach that enhances PD detection through multimodal fusion of time-series signals and PRPD patterns. Our method innovatively integrates these modalities by leveraging the statistical features from PRPD to enrich the time-series data, thereby extracting features that are both hardware-robust and domain-invariant. This dual-modal fusion provides a deeper insight into the characteristics of PD, enabling better discrimination among different types of discharges. Furthermore, our strategy emphasizes the learning of domain-invariant features, ensuring the model’s flexibility across different hardware setups and environmental conditions. We conducted comprehensive experiments with data collected from multiple sensors under varied conditions, demonstrating the superiority of our method compared to existing methods. The results reveal a significant improvement in PD detection accuracy and robustness, highlighting the practical utility of our method in complex and varied field environments.

To ensure the effectiveness of ultra-high-frequency (UHF) method on the on-line monitoring and live detecting of partial discharge (PD) for the gas-insulated metal-enclosed switchgear (GIS), this paper proposes a field calibration method for the UHF PD detection sensors in GIS, which is based on the forward propagation coefficient in the microwave network theory. Firstly, on a full-scare test platform of a 252 kV GIS, the forward propagation coefficients between several pairs of UHF sensors were measured, which agreed well with the frequency spectrum characteristics calculated by the pulse injecting method indirectly and those of PD with different defect types. Then, the criterion corresponding to the detection sensitivity of 5 pC for the proposed method was studied, that is the mean value of forward propagation coefficient between 300MHz and 1.5GHz is not less than −75 dB. Finally, the proposed method including the criterion was applied to the field calibration for the sensitivity of the PD online monitoring UHF sensors in an 1100 kV GIS. Through the above research, differentiated arrangement of UHF sensors was suggested for the on-line monitoring and live detecting of PD in GIS.

The paper describes the research on partial discharges (PD) characteristics in various types of power transmission lines insulation defects. Thirteen types of insulation defects were considered. PD measurements were performed using the AC high voltage source and commercial PD registration device. Phase-resolved partial discharge (PRPD) patterns, average apparent charge, and PD intensity were obtained for each defect type. The research results showed that most overhead lines insulators defects had relatively high PD intensity with small average apparent charge. Several insulators’ defects showed a low average charge and intensity, but insulators’ bodies in these cases were cut and could be destructed by mechanical loads. Power cable samples showed relatively high intensity and average charge in case of the cable termination defect. However, the needle grounding electrode defect showed considerable average charge and intensity. The obtained results will be useful for the defect type recognition tools development and PRPD patterns base extension.

In order to improve the effectiveness of partial discharge detection in attached metal particle insulators, this paper proposes a partial discharge detection method for particle defects in insulators under high-frequency sinusoidal voltage excitation. In order to study the development process of partial discharge under high-frequency electrical stress, a two-dimensional plasma simulation model of partial discharge with particle defects at the epoxy interface is established under plate–plate electrode structure, which realizes the dynamic simulation of particulate defect partial discharge. By studying the microscopic mechanism of partial discharge, the spatial and temporal distribution characteristics of microscopic parameters such as electron density, electron temperature, and surface charge density are obtained. Based on this simulation model, this paper further studies the partial discharge characteristics of epoxy interface particle defects at different frequencies, and verifies the accuracy of the model from two aspects of discharge intensity and surface damages through experimental means. The results show that with the increase in the frequency of applied voltage, the amplitude of electron temperature shows an increasing trend. However, the surface charge density gradually decreases with the increase in frequency. These two factors make partial discharge severest when the frequency of the applied voltage is 15 kHz.

This paper proposes a practical partial discharge (PD) severity analysis based on three factors as repetition rate, amplitude and gap time between positive and negative pattern on a phase resolved partial discharge (PRPD) pattern. The PDs are measured by using conventional and unconventional method. The PD amplitude represents PD size. Repetition rate is an average number of PD pulses per second. Gap time is a time distance between two clusters on positive and negative sides in PRPD pattern. Seven 6.6 kV motors were investigated by analysing three parameters, the results show that seven motors encountered high, moderate and low PD severity according to amplitude, repetition rate and gap time. In the first motor, the PRPD pattern shows slot discharge with a high repetition rate and a narrow gap time. This indicates the high risk to motor failure within a short period. The slot discharge was found at stator conductor through ground wall insulation to stator core. In the second motor, the PRPD pattern shows the slot discharge with high repetition rate with the gap time about 5.88 ms. Subsequently, a conventional method according to IEC60270 was performed. The problem was confirmed by visual inspection as stator slot damage by loosen wedge. Finally, the repetition rate, amplitude and gap time analysis were performed with another 5 motors. The results show the different PD severity that were further investigated and confirmed by visual inspection. This PD severity analysis could help to easily analyse, identify problem, predict PD severity and possibility of failure.

This study is focused on solution to the problem that may occur due to noise signals when using the image obtained through the conventional partial discharge preprocessing methods as inputs to the CNN-based partial discharge pattern classifier. To solve such problem, a new data preprocessing method is proposed by considering a projection technique. In the data information obtained through the conventional partial discharge preprocessing methods, data information of useless noise signal leads to the problem of performance degradation when constructing highly qualified pattern classifier based on the data information of partial discharge signal. The proposed preprocessing method called as ‘Projection’ in this study is designed to solve this problem and improve a classification performance of CNN-based partial discharge pattern classifier. First of all, through GIS simulation, one-dimensional partial discharge data is obtained as five cases such as corona discharge, floating discharge, insulator discharge, free particle discharge, and steady state (noise) in an environment with a noise signal by using a UHF sensor. After that, by applying the two conventional partial discharge preprocessing methods such as PRPS(Phase Resolved Pulse Sequence), PRPD(Phase Resolved Partial Discharge) and the proposed partial discharge preprocessing method, one-dimensional partial discharge data is transformed into 3 data types such as image set of PRPS, image set of PRPD, and image set of Projection. Finally each image set is used as inputs to the designed CNN-based partial discharge pattern classifier. Through the comparative analysis of the feature maps of each layer in CNN as well as the performance accuracy of partial discharge pattern classification for each image set, the superiority of the proposed preprocessing method is demonstrated.

This paper presents an investigation on the off-line Partial Discharge (PD) and localization for 1 unit of in-service 5 MVA 33/6.9 kV oil immersed transformer. First, Dissolved Gas Analysis (DGA) was performed to confirm the existence of PD activities. Next, offline PD measurements were carried out on both LV and HV windings. Once the transformer has been energized, PD localization was performed at different sections of the transformer tank. It is found that HV windings are having possible multiples PD activities based on offline Phase-Resolved Partial Discharge (PRPD) pattern and PD localization.

The reliability of electrical assets is greatly influenced by the quality of their insulations. Key power installations such as power cables are manufactured with polymer-based materials as part of their insulation system. However, accelerated ageing of equipment insulations due to manifestation of defect(s), and partial discharges (PDs) can offset the operation of these systems or even lead to breakdowns. In this study, a non-deterministic model to simulate the phenomenon of repetitive discharges in a spherical air-filled cavity within a practical power cable has been investigated. In addition, the work contributes to the understanding of PD behaviour and field distribution under different ageing conditions considering changes in cavity surface conductivity. First, a section of the practical XLPE cable containing the cavity is developed in 2D using COMSOL software, and a finite element analysis (FEA) of the electric field distribution within the cable insulation is performed. The magnitude of the cavity local field, that is enough to ignite a PD, is investigated. Alongside the COMSOL model, the activity of sustained internal PD is simulated in MATLAB by introducing a random sample generating factor and adjusting the model’s parameters to obtain something close to the practical results. Furthermore, the impact of continuous PD in the power cable under different cavity dimensions and surface conductivity is likewise investigated, and a phase resolved PD (PRPD) pattern is established. The result shows that the magnitude and number of PDs per cycle increase as the cavity size and its surface conductivity increase. Finally, when the cavity surface conductivity rises, the amplitude of the electric field generated by the surface charge distribution and the number of PDs per cycle approach their maximum values.

Partial discharge detection on power equipment using a magneto-resistive sensor

The development of defects in basin insulators inside a GIS (gas-insulated metal-enclosed switchgear) could trigger partial discharges under long-term working voltage, threatening safe GIS operation. In this paper, the ultrasonic pulse-echo method was proposed to detect large-scale basin insulators. And an ultrasonic pulse-echo system was set up, including an ultrasonic generator, a transducer and so on. Among them, the ultrasonic generator has been improved to obtain the original ultrasonic waveform. Then ultrasonic tests were carried out on the thickness of large-scale basin insulators, and the type and appropriate frequency of the ultrasonic transducer was proposed. Besides, a void in the epoxy composite specimen was detected and located. The results showed that the ultrasonic pulse-echo method could penetrate 15mm $\sim70$ mm thick basin insulators, and the 1P20 transducer has a better effect on detecting defects than the 1.5P14. The ultrasonic pulse-echo method is feasible for detecting defects in large-scale basin insulators.

A computerized pattern recognition system based on the analysis of phase resolved partial discharge (PRPD) measurements, and utilizing genetic algorithms, is presented. The recognition system was trained to distinguish between basic types of defects appearing in gas-insulated system (GIS), such as voids in spacers, moving metallic particles, protrusions on electrodes, and floating electrodes. The classification of defects is based on 60 measurement parameters extracted from PRPD patterns. Classification of defects appearing in GIS installations is performed using the Bayes classifier combined with genetic algorithms and is compared to the performance of the other classifiers, including minimal-distance, percent score and polynomial classifiers. Tests with a reference database of more than 600 individual measurements collected during laboratory experiments gave satisfactory results of the classification process.

Investigation of partial discharge characteristics in XLPE cable insulation under increasing electrical stress

Dry-type transformers require little maintenance and are more environmentally friendly with respect to traditional oil-and-paper design. However, partial discharges (PDs) and thermal stress may lead to aging and—eventually—failure of the insulation. Diagnostics of their conditions—often performed by means of PD measurement—can be tricky, as PD activity depends on many parameters. In this work, a cast-resin power transformer is aged by means of 28 heating/cooling thermal cycles, to reproduce the thermal–electrical stress it may experience in operation. PD activity is monitored during the cooling branch of each cycle using a high-frequency current transformer (HFCT) to measure the current bled to the ground by the transformer shield, whose results are compared to the apparent charge measured by an IEC 60270-compliant commercial system installed on the medium-voltage (MV) winding. The effect of temperature and aging (number of cycles) is then investigated. The results show good agreement between the two measuring systems, in terms of both the phase-resolved partial discharge (PRPD) patterns and the average intensity trend over time. According to the intensity dependence on temperature, two PD series were identified: one series shows a loose dependence on temperature, and the other one a strong dependence. It was speculated that two types of defects may exist, whose geometry (and hence PD activity) may be affected by temperature to different extents (e.g., due to their position). Finally, a decrease in PD rate in time was observed, without a significant reduction of the average discharge intensity, which is supposedly due to different aging rates of different defects.

Partial discharge (PD) characteristics of hexadecane were studied in a needle-plane electrode geometry under AC field with a 20 μm tip radius tungsten needle. PD experiments were conducted on samples containing known concentrations of aromatic and corrosive sulfur compounds. Partial discharge inception voltage (PDIV), streamer repetition rates, and phase resolved PD (PRPD) patterns were acquired. Ionization potentials (IP) and electron affinities (EA) of hexadecane and the additives were calculated with density functional theory (DFT) and correlated with the PD characteristics. Low IP and negative EA of the additives relative to hexadecane increased the number of positive and negative streamers initiated, except for additives that contained an electron donating group on the aromatic ring. Low IP and positive EA for polyaromatic hydrocarbons (PAH) and corrosive sulfur compounds decreased the number of negative streamers initiated due to large electron capture cross sections. Copper sulfide particulates caused the greatest changes in PD characteristics as a result of its semi-conductive nature.