Partial Discharge Signals Research Articles

Efficient partial discharge signal denoising method via adaptive variational modal decomposition for infrared detectors

Partial discharge detection is one of the effective means for judging cable insulation condition. However, subject to the operating environment of the cable, the measured signal exists a large amount of noise from infrared detectors. In this paper, we proposed an adaptive variational modal decomposition (AVMD)-based partial discharge signal denoising method, which can reduce the noise disturbance significantly. First, variational modal decomposition (VMD) is optimized by the quantum genetic algorithm, and weighted kurtosis is presented as an optimization index. Furthermore, the wavelet thresholding method is utilized to process the reconstructed signal by VMD. To verify the superity of AVMD, Gaussian white noise and periodic narrowband interference are considered in the simulation. When the input signal-to-noise ratio (SNR) is −6.5202 dB, the SNR of AVMD can reach 8.3716 dB. Compared with the existing methods, AVMD has the best denoising performance. Finally, AVMD is used to process the measured signals. Experimental results demonstrate that it can effectively separate the signal and noise and ultimately retain the signal characteristics for infrared detectors.

Research and application of intermittent partial discharge characteristics and easy-warning system for electric equipment

Intermittent partial discharge has become an essential threat to the safety and stability of high voltage electrical equipment. In order to reduce the sudden insulation breakdown failure due to the intermittent and latent partial discharge of the operating equipment, this paper analyzes the discharge characteristics of the intermittent partial discharge in the constant voltage mode. The UHF partial discharge spectrum of typical intermittent partial discharges is obtained, and the intermittent discharges are divided into low-risk and high-risk categories by adopting PCA and FCM cluster algorithms. Aiming at the characteristics of intermittent partial discharge and the on-line monitoring method of partial discharge, a partial discharge signal collection method based on dynamic frequency is proposed. An intelligent monitoring system of intermittent partial discharge is designed to acquire partial discharge signals initially locate defects. Then the BP neural network method is applied to automatically evaluate and early warn the internal insulation status. The test concluded that the designed monitoring system for intermittent partial discharge could dynamically track and warn the low-risk and high-risk, and evaluate the insulation status of the equipment.

Flexible Planar Monopole Built-in GIS PD Sensor Based on Meandering Technology.

To address the problem of low space utilization of existing rigid Ultra-High Frequency (UHF) sensors for partial discharge (PD) in Gas-Insulated Switchgears (GIS) and the problem of disrupting the electric field distribution inside the GIS. This paper draws on the idea of flexible wearable antennas and introduces planar monopole antennas commonly used in the communication field as GIS PD detection sensors and carried out research on flexible planar monopole sensing technology built into GIS PD. The VSWR of monopole antenna in the UHF low band is optimized by the meandering technique. The size of the designed flexible antenna is 142 mm × 195 mm × 0.28 mm. The simulation and physical test results show that the improved monopole antenna with meandering technology has a VSWR of ≤2 in the frequency bands 570 MHz–830 MHz, 1.38 GHz–1.8 GHz, and 2.2 GHz–2.76 GHz when the bending radius is 0 mm, 200 mm, and 400 mm, respectively. The VSWR in the frequency band 450 MHz–3 GHz is ≤5. A 220 kV GIS PD detection platform was built to test the performance of the designed antenna, and the results showed that the antenna could detect the PD signal after bending deformation with a high Signal Noise Ratio (SNR).

Research on aging state detection of reactor turn‐to‐turn insulation under high‐frequency square wave voltage excitation

In power electronic equipment, magnetic devices are important parts, and their working condition and reliability are closely related to turn-to-turn insulation. With the increasing application of high-frequency square wave voltage in the fields of frequency conversion technology and motor control, the high voltage stress on magnetic devices will accelerate insulation aging, so it is necessary to monitor the aging state of turn-to-turn insulation. At present, researchers mainly detect insulation aging by detecting partial discharge signal. However, this method cannot realize the early monitoring of aging, and the measurement cost is high and the accuracy is low. Therefore, for the dry-type core reactor with epoxy resin as the insulation medium, this paper proposes a turn-to-turn insulation aging state detection method under high-frequency square wave voltage excitation. According to the relationship between the turn-to-turn insulation aging state of the reactor and its parasitic capacitance, the turn-to-turn insulation aging degree of the reactor is predicted by measuring and analyzing the phase information of high-frequency components of the current signal. Finally, the measuring principle and method are introduced and verified by experiments.

Multiple partial discharge sources separation using a method based on laplacian score and correlation coefficient techniques

Partial discharge (PD) activity can be destructive to the transformer insulation, and ultimately may result in total breakdown of the insulation. Partial discharge sources identification in a power transformer enables the operator to evaluate the transformer insulation condition during its lifetime. In order to identify the PD source; in the case of presence of multiple sources; the first step is to capture the PD signals and to extract their specific features. In this contribution, the frequency domain analysis, the time domain analysis and the wavelet transform are employed for feature extraction purpose. In practice, there might be plenty of features, and in each scenario, only some of them may be effective. Therefore, among the extracted features, those useful for discrimination of the multiple PD sources are studied. Then, a method, using laplacian score, and the correlation coefficient algorithms; is developed for feature selection. In order to discriminate among the multiple partial discharge sources, a density-based algorithm spatial clustering of applications with noise (DBSCAN) have been employed to cluster among available PD sources and the noise. The results of some case studies demonstrated the great ability of this method in proper discrimination of multiple PD sources.

Power Interference Suppression Method for Measuring Partial Discharges under Pulse Square Voltage Conditions

Partial discharge (PD) is an important metric for the insulation diagnosis of power equipment. However, its detection is affected by the strong electromagnetic interference generated by pulse square voltage. We therefore propose a power interference suppression method for partial discharges under pulse square voltage based on a quadratic measurement method. We conduct analysis of the topology circuit when partial discharge occurs in the insulation test sample and introduce the basic principle of the secondary measurement method according to the superposition principle and the linear relationship between the square voltages at different peak values. We verify the feasibility of this method by simulating a PD signal with power interference. Subsequently, we use the successive interception comparison method to solve the non-correspondence of the two initial measurement points problem and design and manufacture the transformer turn-to-turn oil-paper insulation test sample and experimental tank. By measuring the PD starting voltage of the insulation test sample under the power frequency voltage, we determined the first measurement voltage under the pulse square voltage and obtained the signal x1(t) to subsequently measure the PD signal x2(t). According to the proposed successive interception comparison method, the signal x1(t) is processed, and the secondary measurement method suppresses the power interference of the measured signal x2(t). We demonstrate that the proposed method effectively suppresses the power interference in PD detection under a pulse square voltage.

Considering the Parameters of Pulse Width Modulation Voltage to Improve the Signal-to-Noise Ratio of Partial Discharge Tests for Inverter-Fed Motors

The improvements in signal-to-noise ratio (SNR) are indispensable to obtain the desired output signal of partial discharge (PD) under square wave pulsewidth modulation (PWM) voltage. In this article, the effect of complex PWM voltage parameters (rise time, fall time, frequency, duty cycle) on the SNR of PD tests is investigated. The results revealed that the sensor's high-frequency gain should be increased to improve the SNR under faster switching. PD impulses are more likely to be submerged in the residual interference under high-frequency voltage. The SNR can be improved by only detecting the PD at the rising front because the PD signal overlaps with commutation noise at the falling front of short duty cycle voltage, such as 0.03% duty cycle. The above results can reference the PWM parameters and improve the SNR in PD tests for rotating machines controlled by power electronics.

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.

Multiscale Fusion Simulation of the Influence of Temperature on the Partial Discharge Signal of GIS Insulation Void Defects

Gas insulated switchgear (GIS) in service may tolerate large temperature changes, which leads to uncertainty in the evaluation of the partial discharge (PD) signal. To clarify the influence of temperature on the discharge signals of GIS insulation void defects, this paper proposes a multiscale fusion simulation method and used experiments to verify it. This method combines the streamer simulation at the micro level and the circuit simulation model at the macro level, and a more accurate simulation signal of GIS insulation void PDs under the influence of temperature can be obtained. Experiments have also been carried out, and a PDs detection experiment platform that can be set in different temperature environments was built. The discharge signals at different temperatures were obtained follow IEC60270. Finally, by combining analyses of simulation and experimental data, the law of the influence of temperature on the PD signal of GIS insulation voids was summarized, and the mechanism of the influence was analyzed. The results showed that the multiscale fusion simulation method of GIS insulation void PD signals proposed in this paper is consistent with the experimental results, which can provide a reference for the digital twin model of power equipment status.

Research on excavation and identification method of typical interference signal during transformer partial discharge test

—In order to remove interference signal and external discharge signal during partial discharge test and get the effective signal of partial discharge, this paper carries out the research of typical interference signal mining and identification method for transformer partial discharge test. Firstly, based on the experience of previous researchers in partial discharge research, 47 feature parameters including statistical, texture and shape features are extracted, and the original feature space is constructed based on them, which provides the data basis for the optimization of the feature space and signal classification identification later. Next, the random forest algorithm is used to measure and rank the importance of the feature parameters, and the original feature space is optimized according to the ranking results to obtain the optimized feature space. Finally, the optimized feature space is used as the input of linear discriminant analysis algorithm and K-nearest neighbor algorithm to classify and identify the local discharge signal and typical interference signal, and the recognition accuracy of these two classification and identification algorithms is compared and analyzed, and the K-nearest neighbor algorithm is selected as the final classification and identification algorithm.

Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF

Low-temperature composite insulation is commonly applied in high-temperature superconducting apparatus while partial discharge (PD) is found to be an important indicator to reveal insulation statues. In order to extract feature parameters of PD signals more effectively, a method combined variational mode decomposition with multi-scale entropy and image feature is proposed. Based on the simulated test platform, original and noisy signals of three typical PD defects were obtained and decomposed. Accordingly, relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram. Afterwards, new PD feature vectors were obtained by dimension reduction. Finally, effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour. Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.

Analysis of Electromagnetic Properties of New Graphene Partial Discharge Sensor Electrode Plate Material.

Advanced sensing and measurement technology is the key to realizing the transparent power grid and electric internet of things. Meanwhile, sensors, as an indispensable part of the smart grid, can monitor, collect, process, and transmit various types of data information of the power system in real-time. In this way, it is possible to further control the power system. Among them, partial discharge (PD) sensors are of great importance in the fields of online monitoring of insulation condition, intelligent equipment control, and power maintenance of power systems. Therefore, this paper intends to focus on advanced sensing materials and study new materials for the improvement for partial discharge sensors. As two-dimensional material, graphene is introduced. The electromagnetic properties of graphene partial discharge sensor electrode plate material are analyzed theoretically. By studying the influence of different chemical potential, relaxation time, temperature, and frequency, we obtain the changing curve of conductivity, dielectric constant, and refractive index. A linear regression model based on the least-squares method was developed for the three electromagnetic properties. Finally, the simulation and experiment verified that the graphene partial discharge sensor has better absorption of the partial discharge signal. This study can apply to the design of graphene partial discharge sensors.

Detection and Analysis of Partial Discharges in Oil-Immersed Power Transformers Using Low-Cost Acoustic Sensors

Partial Discharge (PD) is one of the symptoms of an electrical insulation problem, and its permanence can lead to the complete deterioration of the electrical insulation in high-voltage equipment such as power transformers. The acoustic emission (AE) method is a well-known technique used to detect and localize PD activity inside oil-filled transformers. However, the commercially available monitoring systems based on acoustic sensors still have a high cost. This paper analyses the ability of low-cost piezoelectric sensors to identify PDs within oil-filled power transformers. To this end, two types of low-cost piezoelectric sensors were fully investigated using time-domain, frequency-domain, and time-frequency analysis, separately. Thereafter, the effectiveness of these sensors for PD detection and monitoring was studied. A three-phase distribution transformer filled with oil was examined. PDs were produced inside an oil-immersed transformer by applying a high voltage over two copper electrodes, and the AE sensors were coupled to the housing of the transformer. By extracting typical features from the AE signals, the PD signals were differentiated from on-site noise and interference. The AE signals were analyzed using acoustic signal metrics such as peak value, energy criterion, and other statistical parameters. The obtained results indicated that the used low-cost piezoelectric sensors have the capability of PD monitoring within power transformers.

Recognition of Fused Partial Discharge Patterns in High Voltage Insulation Systems: A Hybrid DCNN and SVM Based Approach

The performance assessment of insulation materials within the components of a high voltage power system depends upon effective condition monitoring techniques of the insulation. One of the important phenomena to be considered in the diagnostics and performance assessment of high voltage insulation materials is the Partial Discharge (PD) measurement. The improved Deep learning techniques help in the effective identification and classification of various sources of PD mechanism. In this work, Deep Convolution Neural Network (DCNN) technique is proposed for the fusion of several features that are extracted from the PD signals. Three-dimensional Phase Resolved patterns of PD images can be used for training the DNN. For the classification of PD patterns, linear and non-linear type of multi-class support vector machine has been proposed. The results obtained are compared with linear SVM and non-linear SVMs with the polynomial kernel, Radial Basis Function (RBF) kernel, and Sigmoidal kernel. The SVM type with a higher pattern recognition rate is identified to be effective in PD pattern recognition and classification. The results of the proposed work show that the fusion approach of PD patterns supports applying huge PD data sets as input, generated by multiple faults, for effective PD pattern recognition.

Elucidation of impedance frequency characteristics of cast resin transformer and application to partial discharge diagnostic method

AbstractIt is desired to establish an insulation diagnosis method for cast resin transformers (CRTrs), which is often used in distribution systems. Partial discharge (PD) detection is considered to be an effective method, but its phenomenon has not yet been clarified. This paper presents investigation on the frequency characteristics of PD generated inside CRTrs to improve PD detection technology. It is clarified that dominant frequency of PD signal detected by a high frequency current transformer (CT) depends on the frequency characteristics of the impedance between the primary and secondary windings. It is suggested that the dominant frequency could be predicted in advance by analysis using a high‐frequency equivalent circuit model of CRTrs. PD diagnostic technology that utilizes the frequency characteristics of the CRTrs impedance has also been proposed. It is found that a PD generated external to CRTrs does not have resonance at the dominant frequency of the CRTrs itself, even though it passes through the CRTrs. In conclusion, it is suggested that the frequency analysis of CRTrs impedance could identify degraded CRTrs and external noise.

Study of Capacitive Coupling Sensor Fused With High Voltage XLPE Cable Joint

A capacitive coupling sensor for partial discharge detection with the fusion of high voltage XLPE cable joint is designed in this paper. The sensor is to address partial discharge signals leading to transmission attenuation and external interference causing poor field detection sensitivity. First, a coaxial waveguide transmission model was established of high-frequency electrical signals in the body and joint. The result showed that the signal transmission attenuation was minimized while the sensor electrodes were closely attached to the outer semi-conductive layer of the body. Second, the equivalent circuit model was constructed of the capacitive coupling sensor fused with the 110 kV straight passing joint. The specific installation location, main structure size, detection bandwidth, and sensitivity of the sensor in the joint area were determined, which was to maximize the coupling output signal amplitude and transfer function amplitude. Finally, a lightning surge voltage test was carried out with the integration of the fusion of the joint voltage thermal cycling. Simulation and measurement show the following: while the sensor is installed in the cable metal sleeve break and the electrodes are closed to the overall semi-conductive layer, there is excellent performance for partial discharge detection in the frequency range of 1–300 MHz, with a sensitivity of 5 pC.

Research on characteristics of acoustic signal of typical partial discharge models

The pulse current method, acoustic and ultrasonic partial discharge (PD) detection, and voiceprint PD detection are commonly used detection methods for the PD detection of power equipment. To study the characteristics of PD signals of typical discharge models based on the principles of the above three detection methods, an acoustic detection experimental system consisting of a needle-tip model and a surface model was built. Acoustic tests were carried out on needle-tip models with different curvature radii and surface discharge models with different lengths of conductive paste. The experimental results showed that acoustic and ultrasonic PD detection and voiceprint PD detection exhibited different sensitivities to the needle-tip discharge models, and the combination of acoustic and ultrasonic PD and voiceprint PD detection was more beneficial for the comprehensive detection of cable PD signals. Based on voiceprint recognition technology, this study drew FFT (Fast Fourier Transformation) diagrams of different types of PD acoustic signals and analyzed the differences in the ultrasonic signal frequency distribution. The frequency band of the voiceprint PD signal of the needle-tip discharge models was concentrated in the range 17–27 kHz, and the frequency band of the voiceprint PD signal of the conductive paste discharge models was concentrated in the range 20–25 kHz. The measurement of voiceprint PD signals in these frequency bands were strengthened when the PD of a cable was detected on-site, which provides the basis for the use of the cable model for on-site PD detection.

Research status of partial discharge detection of power transformer based on pulse current method

There are many important equipment running in the power grid, and the power transformer is one of them. It plays an extremely important role in the stable operation of the power system. Therefore, whether the power transformer can operate safely and stably becomes particularly important. When the power transformer is running online, partial discharge will occur if there are insulation defects, which will further develop into breakdown without timely detection and treatment. The insulation inside the transformer often has certain defects and produces certain partial discharge, and the partial discharge signal is often a high-frequency pulse signal. In this paper, the definition of partial discharge, the generation process of pulse current and the detection method of transformer partial discharge based on pulse current method will be introduced in detail. Several common methods are combed and summarized, the current development status of pulse current method is introduced, and the possible research direction in the future is prospected.

Design and experimental analysis of UHF sensor spiral antenna for partial discharge detection of distribution network switchgear

The ultra high frequency (UHF) sensor antenna for detecting partial discharge (PD) in distribution network switchgear is studied. Because of the long period of insulation deterioration in the switchgear, more attention is paid to the middle and later stage of insulation deterioration, and the frequency of PD signal that needs to be paid attention to is wider. Among the commonly used UHF antennas, Archimedes spiral antenna has a wider working frequency band and excellent low-frequency working characteristics, Therefore, Archimedes spiral antennas of UHF Sensors with different outer diameters are designed by using ANSYS HFSS. The influence of the outer diameter of Archimedes spiral antenna on the S11 parameters of Archimedes spiral antenna is studied, and the partial discharge signal acquisition experiments are carried out. The results show that the partial discharge characteristic signals collected by Archimedes spiral antenna with different outer diameters are different. This provides a theoretical basis for the design of UHF sensor antenna used in switch cabinet.

가스절연지상개폐기 PD 자동진단기법 연구

Partial discharge diagnosis is being carried out to prevent failure of ground switch of distribution facilities. However, the reliability of partial discharge diagnosis is insufficient because the results are different depending on how the experts classify the partial discharge and noise signals. Therefore, in this paper, a method to automatically classify partial discharge and noise signals was studied in order to improve the reliability of the partial discharge diagnosis results of ground switch. Partial discharge and noise signal are obtained through the High Frequency Current Transformer(HFCT). And the features were extracted based on the pulse shape analysis, the standard deviation and gravity center of signal. The features are used as input to neural networks and learned using back-propagation. In addition, the structure of neural networks was optimized through genetic algorithm.