Partial Discharge Pulses Research Articles

Study of Ultra High Frequency Measurement Techniques for Online Monitoring of Partial Discharges in High Voltage Systems

Partial Discharge (PD) activity is a pre-cursor for insulation degradation which may eventually lead to catastrophic failure of the electrical equipment with severe social and economic consequences. It is therefore imperative that PD is detected at its early stages to permit repair or replacement, prior to total failure. The bulky non-planar construction of existing ultra-high frequency (UHF) sensors coupled with the lack of proper feeding techniques and the ultrawide signal bandwidth of PD signals which necessitates the use of digital storage oscilloscope have restricted field deployment of UHF technique for online PD detection. In this work, we present a compact ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.58 \boldsymbol {\lambda } $ </tex-math></inline-formula> ) wideband (0.5-3 GHz) dual arm Archimedean slot spiral with coplanar waveguide feed for PD detection, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {\lambda } $ </tex-math></inline-formula> is the wavelength at center frequency. Numerical simulations of the antenna were compared with the measurements of the fabricated antenna. The fabricated wideband antenna was used to detect PD emissions from a mineral oil test cell connected to 10 kVA 50 Hz transformer. We also present different UHF signal measurement techniques ranging from PD pulse detection to reconstruction of the bandlimited PD signal in the digital domain for online structural health monitoring of high voltage systems. The proposed UHF PD measurement techniques are low cost compared to the conventional approach and indicate the feasibility of an embedded system for online PD monitoring using the fabricated UHF sensor.

Analytical investigations on discrimination of partial discharge defective cable termination among healthy ones with joint sheath‐ground connection

Industrial complexes suffer from crosstalk of Partial Discharge (PD) defects in cable terminations connected to the same medium voltage busbars. This phenomenon happens because the sheath-ground connections of all cables of one switchboard are first fastened together and then connected to ground, which leads to PD pulses being induced from the source termination to others. In this paper, based on mathematical investigations on PD pulse propagation and by introducing an energy-based index, a simple rule-based procedure is proposed for discrimination between defective terminations and healthy ones connected to common busbars. The proposed procedure has been verified by simulations even when there exists noise pollution or multiple defects of different strengths.

Studying Partial Discharge Currents of High Voltage Power Line Suspension Insulators

The article presents the results of theoretical and experimental studies of one of the leakage current components. The nature of this component is determined by the action of partial discharges of suspension insulators of high-voltage power lines. A laboratory bench has been made for carrying out experimental studies. As a result, oscillograms of current and voltage across the insulator have been obtained. A replacement circuit for the processes of partial discharge currents flowing on the surface of an insulator has been developed. Based on the equivalent circuit, a simulation model has been developed in the MATLAB program. By comparing the results of the bench and simulation experiments, the adequacy of the simulation model has been evaluated. A spectral analysis of the characteristics of partial discharge pulses has been carried out. Spectral characteristics allow determining the required operating frequency range of the leakage current sensor.

Evaluating the role of carbon quantum dots covered silica nanofillers on the partial discharge performance of transformer insulation

The article presents the experimental results on the role of carbon quantum dots (CQD) covered silica nanofillers on the partial discharge (PD) properties of transformer oil insulation. The improvement in PD performance of nanofiller blend oil is tested with increased voltage gradient and nanofiller concentration. PD of nanoblend oils for various concentrations of modified silica ranging from 0 to 0.1%wt was measured. PD activity of the test samples is simulated in the laboratory with needle, rod and plane electrode geometry combinations. The facets of PD signals such as PD magnitude, PD inception and time duration of PD extracted from phase-resolved partial discharge patterns were analyzed. Detailed time-frequency domain evaluation of PD pulse is carried out. The enclosure of CQD-SiO2 nanostructures has considerably improved the inception voltage up to 23% than base transformer oil and SiO2 nanofluid. Magnitude of discharge is noticeably reduced by the influence of CQD-treated SiO2 nanoparticles in mineral oil irrespective of the electrode geometry.

Effect of vacuum drying the insulation pressboard on partial discharge characteristics under ripple voltage conditions

The object of the research is partial discharges arising in a sample of the insulation pressboard. In most modern high-voltage direct current transmission schemes based on voltage-source converters six-pulse or twelve-pulse converters are used. The signal waveform on the direct current voltage side of a high-voltage direct current transmission is not pure direct current voltage and it has alternating current component. Since the partial discharge activity under distorted voltage is much different than that under pure direct current voltage, voltage ripples and voltage harmonics are the subject of various studies. This article examines the effect of vacuum drying the insulation pressboard on partial discharge characteristics at direct voltage ripples. The insulation pressboard was dried in a vacuum chamber with a residual pressure of 1 mm Hg. Among all the characteristics of a partial discharge, the main focus has been on the apparent charge of a partial discharge. The greater is the apparent charge value, the stronger is the destructive effect on high-voltage insulation. It was shown that drying the insulation in a vacuum chamber has the greatest effect on reducing the apparent charge of a partial discharge at direct current voltage than at alternating current voltage. After drying the insulation in a vacuum chamber, the amplitude of the partial discharge pulses was decreased by 99.3 % at direct current voltage in comparison with the moistened sample. After vacuum drying, at DC voltage, rare and very low magnitude partial discharges were recorded. The conducted research contributes to the development of methods for partial discharge detection under non-standard voltage conditions.

Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

The article presents research on the influence of the type of UHF antenna and the type of machine learning algorithm on the effectiveness of classification of partial discharges (PD) occurring in the insulation system of a power transformer. For this purpose, four antennas specially adapted to be installed in the transformer tank (UHF disk sensor, UHF drain valve sensor, planar inverted F-type antenna, Hilbert curve fractal antenna) and a reference log-periodic antenna were used in laboratory tests. During the research, the main types of PD, typical for oil-paper insulation, were generated, i.e., PD in oil, PD in oil wedge, PD in gas bubbles, surface discharges, and creeping sparks. For the registered UHF PD pulses, nine features in the frequency domain and four features in the wavelet domain were extracted. Then, the PD classification process was carried out with the use of selected methods of supervised machine learning. The study investigated the influence of the number and type of feature on the obtained classification results gained with the following machine-learning methods: decision tree, support vector machine, Bayes method, k-nearest neighbor, linear discriminant, and ensemble machine. As a result of the works carried out, it was found that the highest accuracies are gathered for the feature representing peak frequency using a decision tree, reaching values, depending on the type of antenna, from 89.7% to 100%, with an average of 96.8%. In addition, it was found that the MRMR method reduces the number of features from 13 to 1 while maintaining very high effectiveness. The broadband log-periodic antenna ensured the highest average efficiency (100%) in the PD classification. In the case of the tested antennas adapted to work in an energy transformer tank, the highest defect-recognition efficiency is provided by the UHF disk sensor (99.3%), and the lowest (89.7%) is by the UHF drain valve sensor.

Self-Adaptive Optimization Assisted Deep Learning Model for Partial Discharge Recognition

In the power system, research is being conducted in diagnosing and monitoring the condition of power equipment in a precise way. The Partial Discharges (PD) estimations under high voltage is recognized to be the most renowned and useful approach for accessing the electrical behaviour of the insulation material. The PD is good at localizing the dielectric failures even in the smaller regions before the occurrence of the dielectric breakdown. Therefore, the PD condition monitoring with accurate feature specification will be the appropriate model for enhancing the life span of the electrical apparatus. In this research work, a novel data-driven approach is introduced to detect the PD pulses in power cables using optimization based machine learning models. The proposed model will encompass two major phases: feature extraction and recognition. The first phase of the proposed method concentrates on extracting the wavelet scattering transform-based features. In the second phase, these features are fed as the input to optimized Deep Belief Network (DBN), whose count of the hidden neuron is optimized via a Self Adaptive Border Collie Optimization algorithm (SA-BCO). Finally, the performance evaluation is done in terms of diverse performance measures.

Error in the measurement of partial discharge pulses according to the frequency response of HFCT sensors

Error in the measurement of partial discharge pulses according to the frequency response of HFCT sensors

Credibility-Based Fault Fusion Diagnosis Method for Power Transformer

The fault causes and phenomena of power transformers are complex; different monitoring parameters can reflect its state from different dimensions. In view of the diversity of monitoring data and the characteristics that the acquisition time of different monitoring data is not completely simultaneous, the power transformer fault fusion diagnosis method is proposed based on credibility theory in this paper. First, the monitoring data, including dissolved gas analysis data, partial discharge pulse signal, ultrasonic signal and vibration signal, are diagnosed, respectively. Then the credibility method is used to fuse the single diagnosis results to obtain the comprehensive state of transformers. The case analysis shows that the proposed method can not only make full use of various monitoring data to enhance the accuracy of transformer fault diagnosis but also apply to the fault diagnosis of transformers with different monitoring data types. This provides a new idea for the fusion diagnosis of power transformers.

Partial Discharge Pulse Segmentation Approach of Converter Transformers Based on Higher Order Cumulant

As one of the most effective methods to detect the partial discharge (PD) of transformers, high frequency PD detection has been widely used. However, this method also has a bottleneck problem; the biggest problem is the mixed pulse interference under the fixed length sampling. Therefore, this paper focuses on the study of a new pulse segmentation technology, which can separate the partial discharge pulse from the sampling signal containing impulse noise so as to suppress the interference of pulse noise. Based on the characteristics of the high-order-cumulant variation at the rising edge of the pulse signal, a method for judging the starting and ending time of the pulse based on the high-order-cumulant is designed, which can accurately extract the partial discharge pulse from the original data. Simulation results show that the location accuracy of the proposed method can reach 94.67% without stationary noise. The field test shows that the extraction rate of the PD analog signal can reach 79% after applying the segmentation method, which has a great improvement compared with a very low location accuracy rate of 1.65% before using the proposed method.

Partial Discharge Localization on Power Cables Based on a Novel Signal Relay System

Time domain reflectometry (TDR) is an effective method used to detect partial discharge (PD) and localize PD sources on power cables. However, for long-length power cables, the attenuation and dispersion of PD signals are significant due to the propagated distance. The attenuation causes a serious reduction of the pulse amplitude, which makes the defects located far away from the PD detector difficult to find. The dispersion causes appreciable asymmetry in the PD pulse wave form. Thus, the localization accuracy is reduced. To solve the problem of long-length cable fault localization, a novel signal relay system is proposed in this paper. Once a weak PD pulse is captured in the signal relay system, another standard Gaussian pulse signal is initiated by a function generator. The Gaussian pulse signal is injected into the cable and travels along the cable. During this procedure, the weak PD pulse is likely to be amplified, and the fault localization accuracy is notably enhanced. To validate the effectiveness of the proposed method, an experimental platform is built in a laboratory, and PD detection of an artificial defective cable is performed. The experimental results show that the signal relay system can effectively “enlarge” the PD pulse on a long-length cable. For long-length power cables, PD detection can achieve a higher localization accuracy based on the proposed method.

МОДЕЛИРОВАНИЕ ИМПУЛЬСА ЧАСТИЧНОГО РАЗРЯДА В ЦЕПИ ЗАЗЕМЛЕНИЯ СИЛОВОГО КАБЕЛЯ

The paper describes the modeling of a partial discharge (PD) pulse in the grounding circuit of power cables in order to evaluate the effectiveness of using a low-resistance resistive element for registering par-tial discharges. Three possible options for constructing the model are considered. The first is based on the classical method for calculating transients. A system of differential equations was compiled, corresponding to the compiled scheme for the occurrence of PD impulses. The system was then solved using the Matlab software package. The Simscape Electrical module built into Matlab/Simulink was chosen as the second option for build-ing the model. A scheme for the occurrence of a partial discharge was drawn up, initial conditions were set, and a solution was made. The third option for constructing the model was a computer model of an electrical circuit using a calibration generator. All three constructed models showed similar simulation results for a PD pulse cor-responding to 3 nC. As a result, it was determined that the resistive element can be used to register partial dis-charges. However, possible registration problems can be interference in the enterprise network. However, it is assumed that dangerous levels of partial discharges can be recorded even in the presence of interference.

Partial Discharge Diagnosis With Siamese Fusion Network

Partial discharge is a common fault type in the operation of power equipment. Recently, deep learning methods have shown great potential in partial discharge (PD) diagnosis. These methods construct a fitting relationship between input and output with mass training samples. Due to the scarcity of PD samples, it is hard to train a classification model. Thus, it is challenging to apply traditional deep learning methods for diagnosing PD. To overcome this issue, this paper introduces a siamese fusion network to diagnose PD. This method comprises three main steps. First, an ultra-high-frequency (UHF) sensor generates two spectrums from power equipment, including phase resolved partial discharge (PRPD) and phase resolved pulse sequence (PRPS). Based on a few-shot learning strategy, a support set is constructed. There are four different types of PD samples and a normal sample. Then, two siamese networks are employed to estimate similarity scores between a test sample and support set samples. One network measures similarity scores in PRPS, and the other measures similarity scores in PRPD. Based on similarity scores, initial diagnosis results are generated. Last, a simple and effective decision fusion technique fuses initial diagnosis results. The final diagnosis result can be generated by jointly exploiting complementary information in two spectrums. With limited training samples, experimental results show that the proposed SFN method can achieve an outstanding diagnosis performance, compared with several classical classification methods.

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.

Characteristics of Partial Discharge on 10 kV Covered Conductor Around Tower Head

The widely use of covered conductor increases the reliability of distribution network. However, a new problem of partial discharge caused by covered conductor at the position of tower head is generated at the same time. This kind of partial discharge will damage the insulation layer or accelerate the deterioration of insulation, which may cause the single-phase grounding fault and affect the power supply reliability. At present, there is few systematic studies on the characteristics of this partial discharge. This paper mainly analyzes the electric field distribution near the covered conductor around tower head, builds the partial discharge experiment platform, and obtains the pulse current characteristics of partial discharge under different working conditions. The results show that the maximum electric field intensity is located at the interface between covered conductor, binding wire and air. The main causes of partial discharge are the wet and dirty of the surface of porcelain insulator and conductor insulation. The minor cause is the breakage of covered conductor insulation. Under different conditions, the initial partial discharge pulse current wave front time is 8ns ~ 45ns, the tail time is 30ns ~ 150ns. The discharge time interval can be as low as 30μs. In addition, it is found that there are two types of partial discharge through spectral analysis. One occurs when the covered conductor insulation is damaged under binding wire; the other is the covered conductor insulation is intact.

Propagation Characteristics of Partial Discharge Pulse Current on 10kV Covered Conductor

With the application of covered conductor in distribution overhead lines, the grounding and short circuit faults caused by foreign objects have been eliminated. But new partial discharge problem has appeared on the surface of the covered conductors around tower head. It may cause the single-phase grounding fault and affect the power supply reliability. To increase the reliability, status monitoring must be used to prevent the faults from beginning. Currently, the partial discharge on overhead lines is usually measured by coils. It utilizes pulse currents to check whether there is a partial discharge. However, it cannot be used in status monitoring as the propagation characteristics is not clear. This paper carried out a simulation study on the propagation characteristics. By constructing a simulation model of overhead lines with covered conductor by the distribution parameters, the initial pulse current amplitude and front time transformation rule in different transmission distance is obtained. Considering different conditions such as pulse current frequency domain (10~50MHz), covered conductor height (4.5~8.0m), covered conductors’ phase spacing (0.6~1.0m), and conductor cross-sectional area (50~185mm2), simulation results show that the amplitude attenuation rate is between 1.5% and 6.3% per 100m, and the front time growth rate is between 1.5% and 5.7% per 100m. The height of covered conductor has the greatest influence on the pulse current waveform transformation, and the main frequency of the pulse current takes the second place, while the difference between the phase spacing and the cross-sectional area has less than 1% influence on transformation.

Reliable detection of pulse arrival time for partial discharge location in HV cable based on improved Allen-Bear algorithm

In multi-sensor data based partial discharge (PD) source location in high voltage (HV) cable, poor reliability of detection of pulse arrival time against noises, waveform distortion, amplitude attenuation is the major barriers to practical application. In this paper, an improved Allen-Bear PD pulse detection algorithm, which combines the advantage of the characteristic functions of the two time-window energy ratio based detection algorithms, is proposed for robust PD location. The most critical contribution of proposed method is the extension of frequency domain component compared to the characteristic functions of the original algorithm. Simulation and physical experiment results demonstrate that the proposed method not only ensures the detection accuracy of the PD pulse arrival time, but also improves the reliability under harsh practical scenarios. It performs significantly better than the existing detection algorithm in terms of anti-noise, anti-waveform distortion, time window length variation, etc. It has significant value for practical applications.

Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition.

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.

Effect of Harmonic Voltage On Partial Discharge Properties of LN2/PPLP Insulation for HTS DC Cable

In this paper, the effect of harmonic voltage on partial discharge properties of LN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /PPLP (Liquid nitrogen/ Polypropylene-Laminated-Paper) insulation system for high-temperature superconducting (HTS) DC cable is studied. The harmonic components with a frequency from 50 to 350 Hz are superimposed on the DC voltage to analyze the mechanism of the harmonic amplitude and order on partial discharge (PD). The experimental results show that when the V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> of the composite voltage remains consistent, the increase in the proportion of the harmonic amplitude will cause the enhancement of partial discharge amplitude, discharge counts and pulse repetition rate, especially as the amplitude of harmonic component exceeds that of DC voltage. The increase of the harmonic order results in variation of the derivative of the applied voltage with time (dv/dt) along the phase, which further causes the pulse repetition rate to increase. It is concluded that the harmonic components superimposed on DC voltage promote the occurrence of PD, which poses a significant threat to the insulation of superconducting DC cable.

Frequency Resolved Partial Discharges Based on Spectral Pulse Counting

A partial discharge (PD) classification methodology based counting PD pulses in the spectral domain is proposed and presented in this paper. The spectral counting data are processed using the proposed PD Spectral Pulse Counting Mapping technique (PD-SPCM), which leads to a Frequency-Resolved Partial Discharges (FRPD) map. The proposed map is then used for PD detection and classification. In this work, corona and slot FRPDs are presented in frequency bands up to 500 MHz, obtained from laboratory measurements performed using two hydro-generator stator bars. The electromagnetic signals from the PDs were captured using a patch antenna designed for this purpose and a spectral analyzer. The corona and slot PDs were chosen because one can be mistakenly classified as the other because they may present similar Phase Resolved PD (PRPD) maps and may occupy shared spectral bands. Furthermore, corona and slot PDs can occur concurrently. The obtained results show that the corona and slot PDs can be properly identified using the developed methodology, even when they occur simultaneously. This is possible because, as it is experimentally demonstrated, corona and slot PDs have appreciable levels of spectral pulse counting in particular bands of the frequency spectrum.