Search

This paper investigates partial discharge (PD) characteristics and breakdown behavior in five electrode configurations fabricated from fiberglass circuit boards, including parallel electrode (PE), triangular electrode (TE), right-angled electrode (RE), and two floating electrode (FE) designs with 0.3 and 0.5 mm insulation gaps. The electrodes are tested according to IEC 60270:2000 using a commercial device with eight samples per configuration. Key parameters such as the PD inception voltage (PDIV) and breakdown voltage are measured. The TE configuration exhibited the highest breakdown voltage of 14.9 kV, with a PDIV of 6.13 kV and RPDIV of 8.04 kV, indicating strong dielectric properties. The RE configuration showed a PDIV of 7 kV, RPDIV of 8.5 kV, and a breakdown voltage of 13.3 kV. The FE_0.5 mm sample exhibited surface discharges, whereas the FE_0.3 mm sample experienced breakdown at 18 kV with an average breakdown voltage of 15.3 kV. The results indicate that the electrode geometry and insulation spacing strongly influence PD behavior and breakdown resilience. The phase-resolved partial discharge (PRPD) patterns at PDIV and breakdown provide further understanding of the dielectric stability. These findings offer critical insights into designing insulation systems under electrical stress. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) detection plays an important role in online condition monitoring of electrical equipment and power cables. However, the noise of PD measurement will significantly reduce the performance of the detection algorithm. In this paper, we focus on the study of a PD noise reduction algorithm based on improved singular value decomposition (SVD) and multivariate variational mode decomposition (MVMD) for white Gaussian noise (WGN) and periodic narrowband interference signal noise. The specific noise reduction algorithm is divided into three noise reduction processes: The first noise reduction completes the suppression of narrowband interference in the noisy PD signal by the SVD algorithm with the guidance signal. The guidance signal is composed of a sinusoidal signal of the accurately estimated narrowband interference frequency component, and the amplitude is twice the maximum amplitude of the noisy PD signal. The second noise reduction decomposes the noisy PD signal after filtering the narrowband interference signal into k optimal intrinsic mode function by the MVMD after parameter optimization. By calculating the kurtosis value of each intrinsic mode function, it is determined whether it is the PD dominant component or the noise dominant component, and the noise dominant component is subjected to 3σ filtering to obtain the reconstructed PD signal. The third noise reduction uses a new wavelet threshold algorithm to denoise the reconstructed PD signal to obtain the denoised PD signal. The overall noise reduction algorithm proposed in this paper is compared with some existing methods. The results show that this method has a good effect on reducing the noise of PD signals measured in simulation and field. [ABSTRACT FROM AUTHOR]

Online partial discharge (PD) detection for power cables is one reliable means of monitoring their health. However, strong interference by white noise poses a major challenge in the process of collecting information on partial discharge signals. To solve the problem whereby the wavelet threshold estimation based on sample entropy falls into the local optimal and the wavelet noise reduction makes it difficult to process detailed information, we propose a partial discharge signal noise reduction method based on a combination of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and discrete wavelet transform (DWT) with multiscale sample entropy (MSE). Firstly, the ICEEMDAN method was used to decompose the original sequence into multiple intrinsic mode components. The intrinsic mode function (IMF) components were grouped using the mutual information method, and high-frequency noise was eliminated using the kurtosis criterion. Next, an MSE model was established to optimize the wavelet threshold, and wavelet noise reduction was applied to the effective component. The ICEEMDAN-MSE-DWT method can retain effective information while achieving complete denoising, which alleviates the problem of information loss that occurs after denoising using the wavelet method. Lastly, as shown by our simulation and experimental results, the proposed method can effectively realize noise reduction for power cable partial discharge signals, thus providing an effective method. [ABSTRACT FROM AUTHOR]

In ultra–high voltage converter stations, the phenomenon of flashover along the outer insulation of dry hollow reactors made of epoxy (EP) /glass fiber (GF) composites poses a potential threat to the stability of the power system. To enhance its flashover performance under varying complex conditions, it is necessary to conduct in–depth research on the flashover mechanism of this material under different voltage forms. This study conducted tests on the flashover voltage, surface space charge distribution, and partial discharge (PD) parameters under varying AC–DC ratios and AC frequencies, and exploring their relationships. The results show that the DC content in the AC–DC ratio decreases, the flashover voltage decreases, the apparent total discharge of PDs increases, and the maximum surface space charge density decreases. The main influencing factor is the increase in the number of seed charges involved in gas ionization. Increasing the AC frequency, the flashover voltage decreases, the apparent total discharge increases significantly, and the surface charge density remains basically unchanged. The main reason is that the change in the number of alternating cycles further increases the number of seed charges. This study reveals the flashover mechanism of EP/GF composites under different voltage forms, which provides theoretical support for subsequent material modification. [ABSTRACT FROM AUTHOR]

In order to address the issue of the accuracy of partial discharge pattern recognition being constrained by unbalanced samples and the deep structure of the deep learning network, a method for partial discharge data enhancement and pattern recognition based on a convolutional autoencoder auxiliary classifier generative adversarial network (CAE-ACGAN) and a residual network (ResNet) is proposed. The initial step involves the preprocessing of the acquired partial discharge signals, with the phase resolved partial discharge (PRPD) spectra subsequently employed as the training samples. Secondly, a CAE-ACGAN is constructed. The model combines the advantages of a deep convolutional self-coding structure and a generative adversarial paradigm to generate high-quality phase resolved partial discharge spectrograms. Subsequently, a ResNet is employed as the classifier for partial discharge pattern recognition, utilising the CAE-ACGAN-enhanced partial discharge dataset for network training to achieve accurate recognition of partial discharge signals. The experimental findings demonstrate that the SSIM and PSNR indexes of the CAE-ACGAN model utilised in this study are 0.92 and 45.88 dB, respectively. The partial discharge pattern method employing the CAE-ACGAN and ResNet exhibits superiority in identifying partial discharges, attaining an identification accuracy of 98%, which is 7.25% higher than the pre-enhancement level. [ABSTRACT FROM AUTHOR]

This study explores the application of an artificial bee colony (ABC) to locate partial discharge (PD) in a test tank based on acoustic emission (AE) approach. Data from a previous AE PD experimental study, which includes the coordinates of 3 AE sensors and the time difference of arrival (TDOA), were used to construct the nonlinear localization equations. It is known that localization algorithms are among the factors that can affect PD localization accuracy, and the ongoing research in this area underscores the need for further advancements in this topic. Therefore, the ABC was proposed to estimate the PD location through a colony of 120 bees, evenly divided into 60 employed and 60 onlooker bees. The employed bees explored the bounded search space, and onlooker bees refined PD locations found by the employed bees through local search. Scout bees were set out whenever a bee exceeded the limit of abandonment to discover possible PD locations in new areas of the search space. After 500 iterations, the optimal solution was the estimated PD location produced by ABC. Comparisons with the genetic algorithm (GA), particle swarm optimization (PSO) and bat algorithm (BA) revealed that the distance error, maximum deviation and computation time for AE PD localization based on ABC are the lowest. The study concludes that the ABC is more suitable for the multi-variable PD localization task than the GA, PSO, and BA due to its effective balance between local search by onlooker bees and global exploration by scout bees. [ABSTRACT FROM AUTHOR]

Laminated Busbars (LBBs) have emerged as the most effective solution for high power electronic devices, offering low parasitic inductance and remarkable current capabilities. However, the design of LBB presents challenges related to criteria such as the busbar’s parasitic inductance and Partial Discharge Inception Voltage (PDIV). As power converter devices evolve, the balance between these two factors becomes increasingly complex, as increasing PDIV through thicker insulation alone also leads to higher parasitic inductance. This paper aims to address this issue by introducing a comprehensive model that correlates the PDIV of the LBBs with various parameters, including geometric aspects and dielectric material properties. A probabilistic approach is also used in order to study the Partial Discharge (PD) occurrence. The model is incorporated into an efficient software package, empowering LBB manufacturers to achieve more streamlined designs that meet PDIV requirements while adhering to other constraints. Moreover, practical tests have been carried out to validate the model with varying temperatures and pressures. It was found that the experimental PDIV values for the LBB samples drop by 19% between 30°C and 200°C at ground level and by 51.4% between 1 bar and 0.145 bar at 30°C. It was also found that the model based on Paschen’s criteria is capable of accurately predicting PDIV in LBB samples as a function of both temperature as well as pressure. It was only off by around 5.9%.

The development of printable ultrahigh-frequency (UHF) antennas as partial discharge (PD) sensors for high-voltage equipment has been extensively studied. However, achieving ultrawideband (UWB) UHF PD sensors frequently requires larger sizes, unsuitable for certain applications requiring compact sensors for dielectric windows in HV equipment. This research objective is to obtain PD sensors with a wider bandwidth (0.3–3 GHz) and a compact size fitting a less-than-100mm-length gas-insulated switchgear (GIS) dielectric window. A circular patch microstrip antenna (CPMA) was chosen for its small size and potential for UWB performance. This paper discusses the design modification of the CPMA to obtain a wider bandwidth for PD detection in GIS. Simulations and lab-scale experimental verifications were conducted to evaluate the optimized sensor. The modified sensor, with a size of 60 × 73 mm², achieved a bandwidth of 3.08–3.14 GHz, a reflection coefficient of -44 dB, and several resonant frequencies of 0.3–2.3 GHz. This is a seven-time wider bandwidth compared to earlier bowtie antennas while keeping a dimension of less than 100 mm². These properties allow for efficient PD detection in GIS and other insulating media. Experimental results indicate the sensor's capacity to reliably detect and analyze PD signals while responding appropriately to variations in voltage. Doi: 10.28991/ESJ-2024-08-05-03 Full Text: PDF

High-performance cross-linked polyethylene (XLPE) is currently employed in ultra-high-voltage direct current (UHVDC) cables, with the electrical tree being an important cause of DC cable breakdown. The comparison of XLPE samples under different manufacturing processes can provide a reference for the progress of cable production processes. This paper compares laboratory-prepared XLPE samples (DC-XLPE) with XLPE samples extracted from actual cables (Cable-XLPE) through electrical tree experiments, X-ray diffraction (XRD), and gel permeation chromatography (GPC). The experimental findings indicate that the breakdown time of DC-XLPE increased by nearly 50% compared to Cable-XLPE, with slower electrical tree growth and lower average discharge magnitude observed. Overall, DC-XLPE exhibited superior resistance to DC electrical tree and partial discharge. XRD and GPC analyses revealed minimal differences in crystallinity and grain size between the two types, with the primary distinction being DC-XLPE's notably higher molecular weight and more concentrated molecular weight distribution. The differences in physicochemical properties may be attributed to more precise and uniform temperature control during the crosslinking process in laboratory settings, as well as a higher removal rate of crosslinking byproducts, ultimately leading to enhanced resistance to electrical tree and partial discharge in DC-XLPE. [ABSTRACT FROM AUTHOR]

Recent advancements in 3D printing technology have enabled the rapid production of complex structures, yet the dielectric performance of 3D printing materials and their potential for manufacturing electrical components remain insufficiently studied. In this study, a capacitor rated at 10 kV with a capacitance of 1 nF was designed and developed for high-voltage applications. During the production of the capacitor, the insulating and conductive parts were fabricated using a 3D printer. While PLA, ABS, ASA, and PETG were employed as insulating materials, aluminum was chosen as the conductive part. Theoretical calculations and the finite element method were used to validate the measured capacitance of the equipment. The performance of the prototype capacitor was analyzed through partial discharge inception voltages (PDIV), dissipation factor (tanδ), and breakdown voltage measurements. Dissipation factor measurements were performed at 2 and 4 kV voltages in the 50–400 Hz frequency range. The performance of employed materials was comparatively analyzed through experimental and simulation results. Finally, the impact of different insulating materials on the dielectric performance of the prototype capacitors was evaluated. [ABSTRACT FROM AUTHOR]

Partial discharge pattern recognition has been established as a standard diagnostic tool for monitoring the operation of electrical equipment. Intelligent state recognition is the development trend of transformer state recognition, but the existing intelligent state recognition has the disadvantages of single model and low recognition accuracy. In order to overcome this shortcoming, a multidimensional information source transformer partial discharge fault identification method based on D-S evidence theory was proposed. Firstly, wavelet packet decomposition was used to extract the energy features of high frequency partial discharge signal and ultrasonic signal. Then, according to the selected feature set, the CNN ( convolutional neural networks) model and CNN-SVM (convolutional neural networks-support vector machine) model were established respectively. Finally, the D-S (dempster-shafer) evidence theory was used to effectively integrate the output results of the two signal recognition models. The results show that using the proposed wavelet packet decomposition energy feature set as input vector, the recognition rates of the two signals CNN-SVM models reach 95% and 81. 67%, which are 3. 33% and 8. 34% higher than CNN respectively. The overall performance of D-S evidence theory fusion method is better than that of CNN and CNN-SVM, and the accuracy and consistency are improved by 3. 33% and 16. 66% respectively. The effectiveness and feasibility of this method are verified. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of the online monitoring spectra of partial discharge in gas insulated switchgear, a partial discharge distributed monitoring system is studied, and distributed sensors are used to simultaneously obtain ultra-high frequency electromagnetic wave signals. Based on the accumulated partial discharge spectra of each sensor, the smooth decoherence noise recognition algorithm is proposed on a long-term cumulative spectral pattern, which is used to analyse the correlation between the spectra of each sensor. Furthermore, a method for determining whether partial discharge is an internal signal is proposed, and its effectiveness is verified by practice, the results confirmed that the algorithm can avoid the misjudgement of partial discharge results caused by distributed sensor interference. [ABSTRACT FROM AUTHOR]

Cable termination is an important part of energy transmission in high-speed trains, and it is also a weak link in the insulation. It is important to determine the insulation status of cable terminals by the detection results of partial discharge signals, but the partial discharge signals in the field test circuit are mixed with a large amount of external corona interference, which affects the detection accuracy. This paper proposes a signal recognition model that incorporates Stockwell transform (ST) and 2DCNN, which in combination with wavelet noise reduction can achieve a high-precision classification effect for partial discharge and corona interference with an accuracy rate of up to 98.75%. By selecting the maximum energy moment in the ST matrix to correct the position of the time window during the recognition of long time series signals, the problem of corona interference being truncated by the time window and being misidentified as partial discharge is overcome, and the generalization ability of the model is enhanced. Experimental results show that the method has an excellent performance in separating partial discharge and corona interference in long time series signals. [ABSTRACT FROM AUTHOR]

To evaluate the deterioration degree of insulation material by monitoring partial discharges (PDs), it is necessary to fully understand the PD behavior prior to breakdown. We performed measurements of temporal variations in the number and magnitude of PDs in a void of epoxy resin. It was found that the number of PDs with larger magnitude is less before breakdown and that there is a decreased tendency of the number and magnitude of PDs with the elapsed time. The tendency is remarkable as the applied electric field is small. Additionally, the time to breakdown becomes short when the scattering of the number of PDs per unit time decreases. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Partial discharge ultrasonic signal monitoring is one of the commonly used methods to determine the insulation status of oilimmersed transformers. However, the on-site noise interference is difficult to avoid and often accompanied by white noise. Therefore, a denoising method based on improved variational mode decomposition and wavelet transform is proposed. Firstly, taking the kurtosispermutation entropy criterion as the objective function, the ant colony optimization is used to determine the optimal decomposition level and penalty factor of the variational mode decomposition, and the noisy partial discharge ultrasound signal is decomposed into multiple intrinsic mode function. Then, the correlation coefficient method is used to divide the multiple intrinsic mode function into noise free function, noise containing function, and noise function. The maximum-minimum permutation entropy criterion is used as the objective function, and the ant colony optimization is used to determine the optimal wavelet threshold and propose an improved wavelet threshold function for wavelet denoising of the noisy function. Finally, the noise free function and the denoised wavelet function are reconstructed to complete the denoising of the partial discharge ultrasound signal. By denoising simulated and measured partial discharge ultrasound signals and comparing with four other denoising methods, the results show that the proposed denoising method has excellent performance. The signal-to-noise ratio and normalized correlation coefficient are average improved by 43.62% and 2.39% respectively compared with other methods, and root mean square error is average reduced by 35.46%. [ABSTRACT FROM AUTHOR]

Silicone rubber (SIR) is a crucial insulating material in cable accessories, but it is also susceptible to faults. In practical applications, mechanical pressure from bending or shrinking can impact the degradation of SIR, necessitating the study of its electrical tree and partial discharge (PD) characteristics under such pressure. This work presents the construction of a test platform for electrical trees under varying pressures to observe their growth process. A high-frequency current transformer is used to measure PD patterns during tree growth, enabling analysis of the effect of PD on tree initiation and propagation under pressure. The experimental results demonstrate a significant decrease in tree inception probability and increase in PD inception voltage under pressure. The pressure also influences the tree structure and PD during the treeing process, where the longest tree with a branch-like structure appears under 800 kPa. The effect of pressure on electrical tree and PD characteristics can be attributed to changes in free volume, alterations in air pressure within the tree channels, and the affected charge accumulation. [ABSTRACT FROM AUTHOR]

Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model's discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The factors affecting the intensification of partial discharges in gas microinclusions in polymer insulation of power cables are described. Such factors include the amplitude, frequency, and shape of the power supply voltage, as well as the size, shape, and orientation in the electric field of the microinclusion. Mathematical modeling shows the dependence of the field strength inside the micro inclusion on its shape in the context of the possibility of reaching the gas breakdown field strength for the initiation of a partial discharge. It has been experimentally demonstrated that an increase in pulsations of the direct rectified voltage increases the intensity of partial discharges in the dielectric. It should be noted that the effects of higher harmonics on the insulation of power cables are additional to the effects of sinusoidal operating modes, and the appearance of harmonics during cable testing and development of recommendations for their operating modes is currently not regulated. References 12, figures 3. [ABSTRACT FROM AUTHOR]

Gas insulated metal-enclosed switchgear (GIS) partial discharge pattern recognition is an important part of the insulation fault diagnosis and state evaluation. To achieve accurate identification of discharge types, a method based on particle swarm optimization deep belief network (DBN) was proposed. The weight parameters of DBN network were optimized by particle swarm optimization (PSO) algorithm to improve the learning ability of the network for partial discharge characteristics. Firstly, a sample set of GIS monitoring data of four types of partial discharge was selected to analyze the proposed method. Secondly, the improved PSO algorithm combined with the sample data was used to determine the initial optimal weight parameters of the DBN network and establish the initial DBN network. Then, the partial discharge recognition model was obtained by training the initial DBN network with training samples. Finally, based on the partial discharge data of GIS equipment of offshore power platform in Bohai oilfield, a variety of different partial discharge identification models were used to analyze the data samples. The results show that the proposed PSO-DBN model can effectively identify the type of partial discharge of GIS equipment, and has a higher accurate recognition rate than the traditional DBN network, back propagation(BP), support vector machine (SVM) and convolutional neural network (CNN). [ABSTRACT FROM AUTHOR]

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity. [ABSTRACT FROM AUTHOR]

Partial discharges in power cables are an inherent phenomenon during their operation, often leading to failures and significant financial losses. Various prediction methods exist, but they often lack sensitivity in detecting partial discharges and fail to pinpoint the location of potential faults. Conversely, the acoustic emission method offers a more effective solution, enabling precise monitoring of partial discharge levels within equipment and accurate localization of faults in power cables. Enhancing the quality of result analysis requires adhering to specific requirements and implementing various procedures to improve diagnostic effectiveness. This paper provides an overview of fault diagnosis utilizing acoustic emission across different evaluation techniques, presenting results obtained from operating various power cables. [ABSTRACT FROM AUTHOR]

Abstract Cellulose particles present a significant concern within the oil‐paper insulation of transformers, posing potential risks to insulation performance. Under the influence of the electric field, the movement of cellulose particles can compromise the transformer's insulation, leading to potential failure. An experimental platform was established to synchronously record particle motion images, partial discharge (PD) pulses, and electric voltage waveforms in oil, aiming to observe the PD characteristics resulting from particle motion under alternating current (AC) voltage and investigate the relationship between different particle motion modes, motion positions, and PD signals. The findings reveal that the phase distribution of PD signals is correlated with the particle motion mode. Specifically, the phase distribution of PD pulses during the back‐and‐forth motion mode is between 4°–94° and 182°–275°. In the suspended oscillation motion mode, the PD pulses phase is concentrated between 20°–84° and 203°–268°. The generation of PD pulses is closely linked to the particle's motion position. PD pulses occur when the particle remains on the electrode during the back‐and‐forth motion mode, generally, PD pulses rarely occur during the jumping process between the two electrodes. In the suspended oscillation motion mode, PD pulses occur when the particle moves upward, but generally do not occur during downward movement. Furthermore, the Pulse Sequence Analysis technique was used to employ the PD characteristics caused by particle motion in transformer oil. The simulation calculations of the electric field distribution for two different particle motion modes show that the particle's motion can cause distortion of the electric field distribution, leading to the generation of PD. The study of the PD characteristics at different particle motion modes and positions obtained contributes to a deeper understanding of the PD induced by cellulose particle motion under AC voltage and provides a reference for the insulation evaluation of transformers.

The paper is devoted to the study of the dependences of the characteristics of partial discharges on magnetic induction. The design of the experimental setup has been developed. It allows applying both high voltage and current comparable to the operating one. The setup includes a high voltage source (dielectric tester), a current flow circuit, a high voltage current transformer and a sample of XLPE insulated cable. The operation of the electrical circuit of the experimental setup is simulated using software. The modeling has shown that if the operational electrical strength of the current transformer insulation is present, the high-voltage potential cannot contact the current flow circuit. After this, modeling of the magnetic field inside the insulating layer is carried out. Based on the developed design, an experimental setup is created. To record partial discharges, an artificial defect is created in a cable sample. The results of magnetic field modeling made it possible to estimate the magnetic induction in the field of an artificial cable defect. Next, experimental studies are carried out to assess the influence of the magnetic field of the cable core current on the characteristics of partial discharges. The measurement results have showed a decrease in the average apparent charge of partial discharges and partial discharge power with increasing current. In addition, waveforms are compared, but no significant differences are found. The magnetic field of the current may influence the PD performance in the long term due to its possible influence on the direction of growth of the electrical tree structure.

The failures of the power system are caused by insulation damages of HV apparatus including transformers, HV cables and generators. They are expensive. In the beginning, insulation failures occure in limited regions of insulation, which is called partial discharge (PD). When PDs are not detected online, they will spread along the insulation and bridge the whole of the insulation that eventually results in total breakdown. Thus, the HV apparatus fails. In this research, different sensors such as HFCT and coupling capacitor required to detect the PD of different HV devices including power transformers, HV cables, switchgears, motors and generators are introduced. The properties of PD signals occurred in HV apparatus is determined by experimental results related to PD signals detected from these HV apparatus. Then, an approach uses the correlation between signals energy is suggested to determine the location of PD occurred in the HV devices. The suitabality of the proposed approach is satisfied by simulating the PD signals in the EMTP-RV software and processing the detected signals by MATLAB software. It is concluded from the experimental outcomes that the suggested sensors can accurately detect the PD signals occurred in the XLPE cables and transformers. The outcomes shown that the suggested method based on the correlation between signals energy can accurately determine the location of PD source in HV devices.

To achieve high-sensitivity live detection for partial discharge （PD） pulses in high-voltage switch cabinets， a detection method based on a second-order circuit is proposed. Firstly， a pulse detection circuit that includes a charged display device and a detection impedance is constructed， and then the electrical parameters of the sensing and display units are measured and analyzed. Subsequently， a second-order circuit suitable for live detection is introduced， and the impact of second-order circuit， capacitance， and inductance parameters of the charged display device on safety， reliability and detection sensitivity is studied. Finally， a test is carried out on the proposed method using a 10 kV metal-clad switchgear and a charged display device. The results reveal that the detection impedance of the second-order circuit does not undermine the safe operation of the switchgear and the charged display device. Its detection sensitivity is 1.6 times that of the high-frequency current transformer mounted on the earth wire of the switchgear.

Present study deals with the acquisition and analysis of different types of incipient discharges in transformer by adopting Ultra-High Frequency (UHF) technique. The Nyquist rate sampling method generates a large number of samples, making it inefficient for developing an online monitoring system. To reduce this, compressive sensing techniques are employed for signal compression and reconstruction. Various compressive sensing methods, including Convex, Non-Convex, Greedy, and Iterative Thresholding, were compared. Orthogonal Matching Pursuit (OMP) was found to be the optimal algorithm, achieving optimal reconstruction time and error at a compression ratio of 45%. The reconstructed signals were compared with the originals using Fast Fourier Transform (FFT), revealing similarities in dominant frequencies. A Long Short-Term Memory (LSTM) machine learning model was used for signal classification, consistently outperforming other algorithms. This study enhances understanding of incipient/partial discharge detection and classification, highlighting the effectiveness of innovative signal processing and machine learning approaches in power system engineering. [ABSTRACT FROM AUTHOR]

The detection of electric fields in the environment has great importance for understanding various natural phenomena, environmental monitoring, and ensuring human safety. This review paper provides an overview of the current state-of-the-art technologies utilized for sensing electric fields in the environment, the challenges encountered, and the diverse applications of this sensing technology. The technology is divided into three categories according to the differences in the physical mechanism: the electro-optic effect-based measurement system, the MEMS-based sensor, and the newly reported quantum effect-based sensors. The principles of the underlying methods are comprehensively introduced, and the tentative applications for each type are discussed. Detailed comparisons of the three different techniques are identified and discussed with regard to the instrument, its sensitivity, and bandwidth. Additionally, the challenges faced in environmental electric field sensing, the potential solutions, and future development directions are addressed. [ABSTRACT FROM AUTHOR]

Power transformers are crucial and require continuous monitoring to prevent failures. Partial discharge (PD), a significant phenomenon, emits electromagnetic waves, acoustic waves, and light, and causes chemical decomposition of insulation materials. Current technology allows a real-time PD monitoring using the Ultra-High Frequency (UHF) method. This contribution aims to design a small printed antenna for internal UHF sensing within the transformer tank, using low-cost Printed Circuit Board (PCB) materials. A printed antenna less than 4 cm x 4 cm was designed and optimized through parameter sweeps to achieve a proper return loss below 1 GHz. Impedance matching is important to achieve an appropriate return loss, and smaller antennas face high impedance mismatching challenges. Adding a resistive loading can solve the impedance mismatching. To study the impact of the transformer tank model as a cavity resonator on the impedance matching of the printed antenna, the antenna was inserted into the tank model through different valves, and the return loss measurement was conducted. Furthermore, a frequency analysis was performed using a spectrum analyzer to identify the optimal frequency range for PD detection, ensuring a high signal-to-noise ratio. Validation of the performance of printed the antenna in PD detection for different PD models was done through a comparison of the Phase-Resolved Partial Discharge (PRPD) patterns obtained through conventional electrical PD measurements according to IEC 60270 and acquired by the printed antenna using the UHF method. This study demonstrates the feasibility of using a small printed antenna for real-time PD monitoring in high-voltage transformers. [ABSTRACT FROM AUTHOR]

The polyethylene naphthalate (PEN) film has been widely applied as a heat‐resistant thin insulation film and sensor film. For the safe and long application of the film in various products, one significant characteristic is the partial discharge (PD) resistivity. In this study, a well‐known IEC(b) electrode is used to measure PD characteristics such as the maximum partial discharge, qmax and fast PD current waveforms at AC peak voltages of 1–3 kVp and 50–1000 Hz over PEN films with thicknesses of 75, 50, or 25 μm. All experiments are conducted at room temperature of ~20 °C. The positive PD current is defined as the current flowing from a high‐voltage electrode to a ground electrode. The positive and negative qmax increased rapidly with the applied voltage increase but remained almost the same for the applied voltage frequency changes. The PD current duration time was less than 40 ns for the positive current and 30 ns for the negative current at all voltages, frequencies, and film thicknesses. It was deduced that the positive current peak magnitude was approximately twice of the negative one at all applied voltages, frequencies, and film thicknesses. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Cellulose particles present a significant concern within the oil‐paper insulation of transformers, posing potential risks to insulation performance. Under the influence of the electric field, the movement of cellulose particles can compromise the transformer's insulation, leading to potential failure. An experimental platform was established to synchronously record particle motion images, partial discharge (PD) pulses, and electric voltage waveforms in oil, aiming to observe the PD characteristics resulting from particle motion under alternating current (AC) voltage and investigate the relationship between different particle motion modes, motion positions, and PD signals. The findings reveal that the phase distribution of PD signals is correlated with the particle motion mode. Specifically, the phase distribution of PD pulses during the back‐and‐forth motion mode is between 4°–94° and 182°–275°. In the suspended oscillation motion mode, the PD pulses phase is concentrated between 20°–84° and 203°–268°. The generation of PD pulses is closely linked to the particle's motion position. PD pulses occur when the particle remains on the electrode during the back‐and‐forth motion mode, generally, PD pulses rarely occur during the jumping process between the two electrodes. In the suspended oscillation motion mode, PD pulses occur when the particle moves upward, but generally do not occur during downward movement. Furthermore, the Pulse Sequence Analysis technique was used to employ the PD characteristics caused by particle motion in transformer oil. The simulation calculations of the electric field distribution for two different particle motion modes show that the particle's motion can cause distortion of the electric field distribution, leading to the generation of PD. The study of the PD characteristics at different particle motion modes and positions obtained contributes to a deeper understanding of the PD induced by cellulose particle motion under AC voltage and provides a reference for the insulation evaluation of transformers. [ABSTRACT FROM AUTHOR]

The ultra-high frequency (UHF) technique offers significant advantages over the conventional partial discharge (PD) measurement method, particularly for online monitoring, 3D localization, and immunity against noise. However, its primary limitation lies in the challenge of calibration due to the impact of various factors such as PD source locations, antenna characteristics, and transformer structures including, active part and tank wall, on the received UHF signals. Currently established parameters such as signals peak-to-peak and energy of signals do not provide a meaningful correlation between received UHF signals strength and factors such as distance and antenna radiation pattern. Addressing these gaps, this paper introduces a novel parameter: the first arrived signal (FAS), derived from the short-time Fourier transform (STFT) of UHF signals. Experimental results demonstrated the capability of the FAS to correlate meaningfully between signal strength and distance from the source, as well as antenna radiation pattern and polarization. The proposed parameter is then utilized to estimate conventional transferred charge using the received UHF signals. Results indicate promising estimation accuracy, particularly when electromagnetic waves directly reach the antenna. This approach offers the potential for a more precise estimation of conventional PD transferred charge, enhancing the capabilities of the UHF method in assessing insulation system health conditions. [ABSTRACT FROM AUTHOR]

This paper presents a method used to identify partial discharge defects in cast-resin power transformers using a back-propagation algorithm. The Rogowski-type partial discharge (PD) sensor was designed with a planar and thin structure based on a printed circuit board to detect PD signals. PD electrode systems, such as metal protrusions, particle-on-insulators, delamination, and void defects, were fabricated to simulate the PD defects that occur in service. PD characteristics, such as rising time, falling time, pulse width, skewness, and kurtosis without phase-resolved partial discharge patterns, were extracted to intuitively analyze each PD pulse according to the type of PD defect. A backpropagation algorithm was designed to identify PD defects using a virtual instrument (VI) based on the LabVIEW program. The results show that the accuracy rate of back-propagation (BP) algorithm reaches over 92.75% in identifying four types of PD defects. [ABSTRACT FROM AUTHOR]