Partial Discharges Characteristics Analysis in Power Transmission Lines Artificial Defects

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.

The detection of partial discharge (PD) in transformers is of great significance for preventing insulation defects from developing into insulation failures. Transformer partial discharge detection technology includes pulse current method, radio frequency measurement method, UHF method, ultrasonic detection method, gas chromatography, and optical detection method. Among them, the optical detection method has the characteristics of strong anti-electromagnetic interference ability, and the signal can be transmitted and received without loss. The optical detection method has unique advantages. In this paper, an optical signal measuring device for partial discharge inside the transformer is designed and fabricated, the typical insulation defect inside the transformer is simulated, and the optical signal characteristics of typical partial discharge are obtained. Compared with the pulse current method, it is found that the magnitude of partial discharge can be characterized by the integral of the optical signal. The characteristic of the optical signal phase resolved partial discharge (PRPD) spectrum of typical insulation defects is obtained. The PRPD spectrum of the optical signals of different defects is obviously different. It is feasible to draw the PRPD spectrum and identify the defect types according to the optical signal, which can be further applied to the identification of internal faults in transformers.

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.

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.

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

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 paper proposes a polar coordinate pattern approach to characterize the floating electrode defect in gas‐insulated metal‐enclosed switchgear (GIS). A 220‐kV GIS bus bar cavity is taken as an object to design the physical model of the floating electrode defect. The partial discharge (PD) development process is studied when the applied voltage is gradually increasing. The phase‐resolved partial discharge (PRPD) patterns and the polar coordinate patterns at different voltage levels are accessed through phase‐resolved pulse sequence (PRPS) data in the ultrahigh‐frequency (UHF) measurement. The proposed polar coordinate pattern approach does not use the traditional positive half‐cycle and negative half‐cycle analysis method in the PRPD pattern. It uses the clustering results to analyze the characteristics of the PD. It can be concluded that there are several advantages to the polar coordinate pattern. First, in the polar coordinate pattern, the phase is in a head‐to‐tail arrangement, so the observation of the PD phase characteristics can be more intuitive. Second, the polar coordinate pattern makes the calculated phase median, amplitude median, and centroid meaningful. With the voltage increasing, the variation law of the phase median and centroid can be observed. Third, the discharge parameters, which are based on the quadrant and statistical theory and the cosine similarity proposed in this paper, are from a new point of view. They can make a clear description of the characteristics of the floating electrode defect quantitatively, which can be distinguished from other insulation faults. Additionally, the continuous mode and phase mode at different voltage levels measured by acoustic detection are obtained to further demonstrate the PD development process. The acoustic results agree with the UHF measurement results. The efficiency and rationale of the polar coordinate pattern are further verified. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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.

Partial Discharge (PD) characteristics of typical defects in oil-paper insulation under AC, DC and AC-DC combined voltages have been studied extensively. However, there is still limited material available for the pattern recognition of partial discharge under actual stress withstood by valve side windings in converter transformers. In this paper, the PD of needle-plane defect is measured under actual stress in converter transformers. The stress is DC voltage superimposed harmonics, which has been analyzed in PSCAD at first. Then DC voltage superimposed both single harmonic and multiple harmonics, including simulated actual stress and approximate square waveform, have been applied on electrode. PD parameters and patterns are acquired by detector using pulse current method. The test results indicate that the inception voltage and pulse repetition rate of PD have no direct relationship with harmonic order due to the accumulation of space charges injected by DC voltage. PD amplitude shows a growing trend and phase distributions of PD expand with the increase of harmonic order. PD patterns are also closely related to the waveform of applied voltage. PD amplitude rises with the increase of voltage change rate, and the majority of PD pulses occur in the phase range which has a higher voltage change rate. Experimental results show that harmonic order exerts a great effect on PD characteristics of needle-plane defect under DC superimposed harmonic waveforms. The findings of the current study are useful in further research on the pattern recognition of PD and diagnose in converter transformers.

Six models of typical insulation defects are designed and lab-simulated system used for partial discharge (PD) detection of cables and accessories has been constructed to study PD characteristics of internal insulation defects in 110 kV XLPE cable system. By employing CPDM, a kind of on-line PD detector, test data of various models including related PD diagrams are obtained for our research. Based on these characteristic parameters, discharge characteristic analyses of a single PD pulse are presented and statistical operators can be obtained by numerical calculation. The experimental results show that CPDM on-line detecting method is effective for detecting PD signals from internal defects of cable accessories. In addition, there are significant differences in the some statistical parameters (e.g. discharge phase, discharge repetition rate, etc) and characteristic parameters of time-domain PD waveform which can be used as reference points for PD patterns identification in future research.

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

In order to effectively extract the features of partial discharge (PD) signals in GIS, a method of pulse waveform feature extraction based on Hilbert transform was proposed. The PD data of four typical insulation defects were collected by using GIS PD test platform built in the laboratory. The envelope of PD signal was obtained by Hilbert transform, and the statistical characteristic parameters, time domain characteristic parameters and frequency domain characteristic parameters of PD pulse waveform were extracted based on the envelope. Kernel principal component analysis (KPCA) was used to reduce the dimension of PD feature parameter, and random forest classifier was used to classify and identify PD feature parameters after dimensionality reduction. The identification results show that the proposed method can extract the features of PD signals effectively, and the extracted feature vectors can correctly identify PD caused by different insulation defects.

Partial discharge (PD) diagnosis is confirmed to be one of the most effective tools for assessing the health condition of power equipment. Classification and recognition of the measured PD data provide the insulation defects information which facilitate the condition diagnosis of electrical apparatus. This paper presents a new group of image features for partial discharge classification, where the gray images are formed to represent different PD defects. The PD gray images are decomposed into various vectors by two-dimensional linear discriminant analysis (2DLDA), where 9 representative parameters are extracted from each image vector. Finally, fuzzy k-nearest neighbor classifier (FkNNC), multi-class support vector machine (MC-SVM) and back-propagation neural network (BPNN) are respectively employed for PD classification. 419 diversified samples of PD data acquired from typically artificial defect models of oil/pressboard insulation, where the defect size, applied voltage and insulation aging are taken into account, are employed for algorithm validation. The recognition results of 419 PD samples show that the defects are well identified by the proposed 2DLDA features with high accuracy. In addition, the significant increments of average recognition accuracies are obtained by different classifiers compared with the phase-resolved partial discharge (PRPD) features in previous works. The obtained results indicate that the proposed 2DLDA features are potentially effective and reliable in recognizing different PD sources and may be considered as an improved PD recognition tool when compared with the intensively used PRPD features.