Phase Resolved Partial Discharge Analysis Research Articles

Deep Learning and Long-Duration PRPD Analysis to Uncover Weak Partial Discharge Signals for Defect Identification

This study focuses on improving the defect recognition accuracy under weak partial discharges (PDs) in epoxy resin through phase-resolved partial discharge (PRPD) analysis. The method is to refine the data rather than enhance the algorithm. Two measurement conditions are compared until PRPD pattern saturation is achieved: one-minute and one-hour durations. The PD data specifically target three void types located at different positions within the epoxy material. The aim is to evaluate how the presence of weak PDs at the PD extinction voltage (PDEV) influences defect recognition accuracy. This research sheds light on the potential implications of neglecting the significance of weak PD signals in defect detection. A convolutional neural network (CNN) model is trained using PRPD data recorded at the repetitive PD inception voltage (RPDIV) and tested using the new PRPD data from both conditions recorded from a lower PDIV to a PDEV. The trained CNN model achieves a defect recognition accuracy of 100% for a one-hour duration, highlighting the importance of not neglecting weak PD signals. This emphasizes the significance of extended measurement duration and pattern saturation in capturing and analyzing weak PD signals for an improved defect recognition. This study contributes to the advancement of practical applications by understanding the behavior of the epoxy material and enhancing defect detection techniques.

Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence

This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.

Design of a Flexible UHF Hilbert Antenna for Partial Discharge Detection in Gas-Insulated Switchgear

This letter presents the design of a new flexible Hilbert antenna for built-in detection of partial discharges (PD) in gas-insulated switchgear (GIS). The fractal antenna is designed on flexible polydimethylsiloxane (PDMS) material of 124 mm × 122 mm × 0.5 mm, fed by CPW, and loaded with one circular slot and two rectangular slots in the ground plane of each subblock, and with a rectangular slot at the narrow opening to broaden the matching band and improve the gain of the antenna. The actual bandwidth of the antenna without bending is 422–800 MHz, 1.23–1.58 GHz, 1.65–1.7 GHz, and 1.79–3 GHz, covering 73.6% of the detection band, and the bending deformation has little effect on the antenna bandwidth. The measured peak gain of the antenna at a bending radius of 350 mm reaches 1.34 dB, exceeding the reported Hilbert antenna. The test and analysis of the antenna sensitivity are performed by 220 kV GIS PD experimental platform. Experimental results show that the flexible antenna designed in this article can effectively detect the high-frequency electromagnetic wave signal radiated by PD with a discharge amount of about 46 pC. The phase-resolved partial discharge analysis yields that the designed antenna has a high PD detection sensitivity.

AI기법과 고주파 전류 변환센서를 활용한 배전반 on board 진단시스템

This paper presents an on-board switchboard diagnosis system based on the Ai algorithm for the purpose of establishing a real-time partial discharge monitoring diagnosis system. For classifying pattern of partial discharge, FCM-based RBFNN is applied to on-board diagnosis system and K-means, NN(Neural Networks) are used for comparative analysis. The data are obtained by HFCT(High Frequency Current Transformer) sensor from designed partial discharge simulation environment and its phase is divided into 128 degree. Commonly used partial discharge data analysis methods such as statistical analysis, PRPS(Phase Resolved Pulse Sequence) and PRPD(Phase Resolved Partial Discharge) are introduced. In this paper PRPDA(Phase Resolved Partial Discharge Analysis) and PCA(Principal Component Analysis) are used as a pre-processing. The Ai algorithm comparison is performed twice in total. Firstly, a comparison analysis of the test data validation of each model trained in the Python environment is conducted through a confusion matrix. Afterwards, an on-board diagnostic device is added to the partial discharge simulation circuit, and the judgment results for the actual operation are compared. Through comparative analysis in virtual and real environments, it is confirmed that the case in which FCM RBFNN is mounted shows excellent performance.

Mechanism Characterization and Nondestructive Inspection Method of Thermal Degradation Faults in EPDM Cable Termination

There is a significant positive proportional correlation between the condition of ethylene propylene diene monomer (EPDM) insulation and partial discharge(PD) characteristics in electric multiple units (EMU) cable termination. Especially for the cable terminations with long service life, the aging effect of EPDM insulation material is significant due to long-term high-current load. In this paper, electrical deterioration and PD development of EPDM insulation under long-term thermal degradation is conducted. During the test process, the phase-resolved partial discharge (PRPD) analysis, partial discharge inception voltage (PDIV), physicochemical properties, and flashover voltage of EPDM samples with different thermal degradation are measured. The relationship between the thermal degradation extent of EPDM insulation and PD characteristics is also proposed. The results show that electrical deterioration of the aged sample can be divided into four gradations by PD amplitude and pulse repetition rate. In the four gradations, the shape of the PRPD spectrum in every gradation changes significantly with different thermal degradations. Surface morphologies of EPDM insulation show different features such as gully areas and white particles. The results of the Fourier transform infrared spectroscopy(FTIR) test indicate that the increase of oxygen-containing groups and free radicals are the key reasons for the rapid PD development process of cable insulation under different electrical gradations and thermal-oxygen degradations.

Fluorescent fiber-based identification of incipient discharges in liquid nitrogen

Identification of incipient discharges is a major challenge in liquid nitrogenbased super-conducting power apparatus. In the present study, a variety of fluorescent fibers with different absorption/emission characteristics were used for the identification of corona and surface discharges, and we find that a particular type of fluorescent fiber is more sensitive to identify discharges in liquid nitrogen insulation than the UHF technique. Enhancement in the magnitude of fluorescent signal/UHF signal is observed for an increase in applied voltage beyond the corona inception voltage (CIV). Also, it is observed that while reducing the applied voltage, the corona activity quenches at a much lower voltage than the inception voltage. No characteristic variation in rise time or the fall time of the fluorescent signal is observed on corona inception and due to discharges at higher voltages. The corona inception voltage is least for AC voltage conditions, and slightly higher inception voltage is observed for positive DC and negative DC voltage conditions. Different weight percentages of MgO doping in GFRP nanocomposites are prepared and used in the surface discharge studies. It is observed that the 3 wt% sample exhibits enhanced surface discharge inception voltage (SDIV) compared to samples with other doping concentrations and that the inception voltage is lower for AC voltage when compared with the DC voltage conditions. The characteristic spectral content of the fluorescent signal due to corona and surface discharge activity is observed up to 3 MHz. A phase-resolved partial discharge (PRPD) analysis was also carried out to identify corona and surface discharge activity using fluorescent fiber sensor and with a UHF sensor. From the PRPD studies, it is observed that the corona discharge occurs near the peak magnitude whereas surface discharge is observed at the rising part of AC voltage.

Phase-resolved partial discharge analysis of different types of electrode systems using machine learning classification

Partial discharge (PD) measurement is a diagnostic technique used for electrical insulating systems. The establishment of a highly accurate PD diagnostic technique has become necessary in recent years. Therefore, in this study, we analyzed a phase-resolved PD signal using machine learning classification for different types of experimental electrode systems. A polyethylene sheet was used as the sample, and PD was generated by applying a high AC voltage to four different types of electrodes. The number of PD pulses was counted from the raw data as preprocessing to calculate the feature value for the machine learning. The PD generation rate was defined for each phase angle section. Four types of machine learning algorithms were adopted for the classification of the electrode system: k-NN (k-nearest neighbor algorithm), logistic regression, decision tree, and random forest. The best accuracy was obtained by using the random forest algorithm (0.97), and it was found that k-NN also demonstrated good performance. The parameter dependencies were also evaluated for each algorithm. Based on the results generated by the random forest, it became clear that there were phase angle sections that were of high importance. The reason for the result was discussed from the perspective of (i) the difference due to the electrical circuit parameters (modified abc-model) and (ii) the stochastic fluctuation of PD signal.

Investigation on the partial discharge characteristics of eco‐friendly nanofluid insulation of corn oil nanofluid

The suitability of corn oil nanofluid as an insulation material is studied herein by analysing the partial discharge characteristics. Experiments are conducted on nano-silica modified corn oil at 0.01, 0.05, and 0.1 wt.% mass fractions. Electrode configurations are used to generate partial discharge (PD) sources such as corona discharge, internal discharge, and surface discharge. Partial discharge inception voltage dependency on electrode geometry is studied. Phase-resolved partial discharge analysis (PRPD) at various test conditions is done to understand the influence of live element geometry that causes PD in real-time operations, Weibull statistical analysis of PD parameters like scale parameter, shape parameter, skewness, and repetition rate is evaluated to understand the influence of nanofiller mass fraction in corn oil. The results illustrate that the addition of silica nanoparticles to corn oil has a significant influence on PD characteristics. The PRPD pattern analysis reveals information about the PD dependency on electrode configuration of the test condition. The addition of nanofillers in optimal concentrations without agglomeration can influence the PD characteristics to a certain degree. The test results may be inferred to suggest corn oil-based silica nanofluids as an alternative biodegradable liquid insulation.

Dielectric properties of mixed mineral and synthetic ester oil

The dielectric properties of 20% synthetic ester oil mixed with mineral oil were studied for its potential applications as an insulator in transformers. The corona inception voltage of mixed oil under AC and DC voltages was measured by the ultra-high frequency (UHF) technique. A statistical study of the breakdown voltage of mixed oil under AC, DC and standard lightning impulse voltages was carried out to obtain a database which would be useful for designing transformer insulation. Further, the mixture was subjected to continuous breakdown studies under AC voltage with lightning impulse voltages, at regular intervals and characterised through measurement of interfacial tension, flash point, ionic mobility as well as UV–vis spectroscopy and frequency-domain spectroscopy studies to understand the liquid's chemical stability. The results of the study have shown that the mixture was stable even after several breakdowns. The ionic mobility and polarisation current have increased, indicating higher losses in the oil. UHF signal energy analysis and phase-resolved partial discharge analysis have revealed a reduction in discharge activity with mixed oils.

Performance of Clustering Techniques of Multiple Partial Discharge Sources in High Voltage Transformer Windings

There are numerous of clustering techniques that have been exploited by researchers in many applications such in medical application, image processing application as well as in high voltage application. Clustering technique is an unsupervised learning algorithm used to identify group structure in a set of data that contain different characteristics. Nowadays, within the latest HV insulation system, there are more than one dielectric media, which contribute to multiple source of partial discharge (PD). Therefore, data identification for PD is significantly vital to discover the kinds of faults that inducing discharges in a HV insulation system. Nevertheless, it is critical that the methodology used for further investigation such as phase-resolved partial discharge (PRPD) analysis is capable of producing a sufficient separation between the clustered data. An experiment was performed to generate a pair of PD sources simultaneously within a winding of the HV transformer. The PD pulses were collected from two measuring points measured by two wideband radio frequency current transformers (RFCTs) at the bushing tap-point to earth (BT) and the neutral to earth-point (NE).The performance oft-Distributed Stochastic Neighbour Embedding (t-SNE), Principle Component Analysis (PCA) and time-frequency mapping based on sparsity roughness at distinguishing multiple PD sources is determined and presented.

Understanding the influence of ambience on thermal ageing of natural ester liquid

Ambience during thermal ageing of ester liquid has a high impact on corona inception voltage (CIV) under AC and DC voltages. The CIV is high with ester liquid thermally aged in nitrogen ambience and is the least with air aged. CIV is high under negative DC voltage followed by positive DC and AC voltages. CIV reduces with increase in harmonic voltage and the reduction is predominant with harmonic voltages with higher total harmonic distortions. Corona activity radiates ultra-high-frequency (UHF) signal with its dominant frequency at 1 GHz. Phase-resolved partial discharge analysis carried out using UHF signal indicates that corona discharges occur at the peak of the voltage waveform. The magnitude of discharges is less in thermally aged liquid but the number of discharges increases. Fourier transform infrared spectroscopy analysis indicates the formation of carbonaceous particles with thermal ageing. Thermal ageing of ester liquid causes to form new chromophoric moieties causing red shift of absorption band in ultraviolet-visible spectra. Fluorescence spectroscopy traces the characteristic variations due to thermal ageing, which can be adopted for condition monitoring.

Partial Discharges in Insulating Systems of Low Voltage Electric Motors Fed by Power Electronics—Twisted-Pair Samples Evaluation

Power electronics switching devices currently represent the dominant technology for supplying low voltage (LV) electric motors. The fast switching processes exert a different class of stress on dielectric insulating materials than standard sinusoidal excitations. Such stresses result in an increase in the dynamic activity of the working electric field, which in turn lead to an increased likelihood of partial discharges (PD). The stator design of low voltage motor is often in form of random-wound windings, where the magnet wires (copper or aluminum round wires coated with thin layer of insulation) form a common system of coils with not precisely defined mutual position of particular turns, resulting in various turn-to-turn and coil-to-coil voltage distributions. Pulse Width Modulated (PWM) voltage waveforms from modern electronic inverters are characterized by very short rise times and presence of repetitively occurring overvoltages that can significantly stress the insulation of feeding cables and motors. These factors influence the inception and dynamics of PD and processes of space charge accumulation in electrical insulation. In this paper investigations performed on round magnet wire twisted-pair samples representing LV motor random-wound winding elements are presented. Special attention was afforded to the twist configurations, observed breakdown voltage and PD activity. To describe the field conditions for the formation of PD in the turn-to-turn insulation system, the results of numerical simulations of electric field distributions for winding wires with different diameters, modeled using the COMSOL program, were analyzed. PD created in the insulating systems of model twisted-pair systems were registered and analyzed using the phase resolved partial discharge analysis (PRPDA) method.

Four-channel signal-receiver optical subassembly module using a thin-film filter-based coarse-wavelength-division-multiplexing demultiplexer for monitoring 22.9-kV XLPE cable joints

We propose a four-channel signal-receiver optical subassembly (ROSA) for partial-discharge signal transmission using a coarse-wavelength-division multiplexing (CWDM) thin-film filter (TFF). The proposed four-channel ROSA consists of an ultrasmall-factor-based demultiplexer module, which is fabricated with a parallel optical block, four CWDM TFFs, and four 28-Gbps integrated positive-intrinsic-negative photodiode blocks. The partial discharge on a 22.9-kV XLPE cable joint was successfully measured using the proposed module with phase-resolved partial-discharge analysis and a pulse-diagnosis system. The experimental results demonstrated the feasibility and effectiveness of the proposed ROSA module for the diagnosis of partial discharge, which is important for monitoring electric power facilities.

Partial discharge development in needle-plane configuration of oil-paper insulation under AC voltage

Condition of insulation materials can be obtained by analyzing partial discharge (PD) characteristics since features of PD are closely related to it. In this paper, electrical deterioration caused by partial discharge of oil-paper insulation under long-term AC voltage is studied. Feature characteristics of PD, such as phase-resolved partial discharge analysis and equivalent time-frequency, were recorded during the test process. Electrical deterioration is divided into four stages: initial, develop, stagnation and erupt stage. Four stages are separated by pulse repetition rate and discharge magnitude of PD. In different deterioration stage, statistical characteristics of PD change significantly. Surface properties of pressboard in different deterioration stage were studied. Test results indicate that the depolarization of cellulose in pressboard is the main reason for development of partial discharge, and discharges occur in different position of pressboard in different deterioration stages.

Analysis of partial discharge features as prognostic indicators of electrical treeing

The aim of this paper is to identify promising indicators for prognosis of electrical treeing. Both phase-resolved partial discharge analysis (PRPDA) and pulse sequence analysis (PSA) are utilized. The prognostic properties of the features are evaluated in terms of monotonicity, prognosability, and trendability. The investigation reveals that PSA has a higher prognostic suitability index than PRPDA. An exponential fit is applied to the feature with the highest suitability index, in order to demonstrate its use for prognostic modeling and prediction of time until breakdown.

A step forward in the characterization of the partial discharge phenomenon and the degradation of insulating materials through nonlinear analysis of time series

The partial discharge (PD) phenomenon is an electrical discharge that affects only part of a dielectric connecting two conductors [1]. This process may occur in the vacuoles of solid insulation, in gas bubbles in insulating liquids, or between dielectric layers. PDs can also occur on spikes or sharp edges of metal surfaces. Generally, the PDs affect the insulator integrity only if it persists for a long time. However, the continuous release of small amounts of energy may cause a slow and progressive deterioration of the dielectric that can lead to its final breakdown [2], [3]. The degradation rate depends more or less on the microscopic structure of the dielectric, the production process, the types of electrical stresses, and other operating conditions [4]. At the beginning of the 20th century, the problem was not even recognized. However, after the middle of the century, along with the introduction of new dielectrics, the development of more compact insulators, and a general increase in the operating voltages, diagnostics of PDs have become a primary issue in electrical engineering [5]. Today, measuring PDs is a common practice even in operational medium- and high-voltage devices. The data gathered are useful to determine the weak points of the components before irreversible damage occurs. Thus, PD detection and recognition have become important tools for the evaluation of insulating degradation in large power devices. The phase ϕ, the apparent charge q, and the occurrence n have generally been accepted as the basic parameters for the pattern recognition of PDs by means of the standardized algorithm termed phase-resolved PD analysis (PRPDA) [6].

Identification of insulation defects in gas-insulated switchgear by chaotic analysis of partial discharge

The recent increase in the number of failures during the installation and insulation of gas-insulated switchgear (GIS) has led to a need for a reliable risk assessment technique. Accordingly, in this study, a fundamental database of UHF partial discharge (PD) patterns corresponding to different types of defects is presented for risk assessment and for the observation and diagnosis of the state of insulation of GIS at field sites; the patterns have been obtained by modelling a number of defects that have been reported to be the most critical in GIS. For the realisation of a system that can simultaneously detect and analyse UHF PD (it is internationally accepted that the use of such a system is the most effective method to diagnose GIS insulation), a wideband UHF sensor and amplifier are designed and fabricated. The system operation is then investigated. In addition, a chaotic analysis of partial discharge (CAPD) is proposed. This analysis can identify the type of defect by means of PD pattern classification without employing the phase information of the applied voltage signal. The proposed CAPD can replace the conventional phase-resolved partial discharge (PRPD) analysis and can be employed at field sites when the phase information is unavailable. Especially, the PD patterns of free-moving conducting particles, known to be very important in GIS, can be distinguished from those of other defects when using the CAPD method, which is not the case when the PRPD analysis is being performed.

부분방전 해석 방법으로 PSA(Pulse Sequence Analysis)의 현장 적용성에 대한 고찰

Because of its effectiveness for the PD(Partial Discharge) pattern recognition, PSA(Pulse Sequence Analysis) has been considered as a new analytic method instead of conventional PRPDA(Phase Resolved Partial Discharge Analysis). However, it is generally thought that PSA has some possibility to misjudge patterns in case of data-missing resulting from poor sensitivity because it analyses the correlation between sequential pulses, which leads to hesitate to apply it to on-site. Therefore, in this paper, the problems of PSA such as data-missing and noise-adding cases were investigated. for the purpose, PD data obtained from various defects including noise-adding data were used and analyzed. As a result, it was shown that both cases could cause fatal errors in recognizing PD patterns. In case of the data missing, the error was dependant on the kinds of defect and the degree of degradation Also, it could be noticed that the error due to adding noises was larger than that due to some data missing.

Partial Discharge Signal Detection by Piezoelectric Ceramic Sensor and The Signal Processing

Partial discharge (PD) in an insulator or on surface of defective conductor emits acoustic wave trans- mitting through an air or an insulator. The acoustic wave between 20 kHz and several hundred of kHz can be detected by piezoelectric ceramic sensor that converts the acoustic wave into an electrical signal. Piezoelectric ceramic sensor has either the wide resonant band or the local resonant band depending on the ceramic material or the various combinations of each different component in the manufacturing process. This paper presents the piezoelectric ceramic sensor with 0.95 PZT-0.05 PMNS that yields the piezoelectric properties of high kp, high Qm .I thas the frequency characteristics of local resonant band, such that it can be applied to PD detection. We have demonstrated the properties of the proposed piezoelectric ceramic sensor by comparing with the conventional electrical PD detector. Quantitative analysis is accomplished by comparing the ψ-qmax from a PD detector and the ψ-vmax from the proposed sensor while ψ-n distributions are the same for both the conventional phase-resolved PD analysis method and the proposed one.