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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.)

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]

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]

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]

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 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]

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]

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]

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

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.

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]

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]

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.

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.

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office 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.)

Electrical machines for transportation applications are subjected to harsh environmental conditions during their operations. Partial discharge (PD), which is one of the main reasons for insulation failure, is greatly affected by ambient conditions (i.e., temperature, pressure, and humidity). Countless efforts are made for a comprehensive understanding of the physics of PD under variable environmental factors. This paper aims to review recent works addressing temperature, pressure, and humidity impact on PD activity. The main content of the paper is organized into three sections dealing with each environmental factor. In every section, relevant publications are reviewed considering the type of samples tested, voltage waveform applied, mutual effects, and the most common PD modeling strategies used. The applicability of the PD measurements for PD risk assessment is also discussed. Based on the review, the current progress in understanding the environmental effects on the PD inception mechanism and PD characteristics is presented and discussed in detail, and future research trends in this field are outlined. [ABSTRACT FROM AUTHOR]

The current research mainly focuses on the influence of different voltage forms on partial discharge for the interturn insulation of a transformer, and the discharge characteristics and its mechanism of interturn insulation under the action of preload are unclear. Therefore, a partial discharge test platform under the synergistic action of preloading force and electrical stress is constructed based on the actual operation conditions of the interturn insulation of a 750 kV transformer. Then, the partial discharge characteristics and its mechanism is explored by using (OM, SEM, FTIR, XRD, EDS, FEA). It is found that the statistical parameters and damage degree of interturn insulation decrease first and then increase with the increase in preload. Moreover, there is an inflection point at 1000 N. The reason is that the preload causes the deformation of the holes and air gaps between the layers of insulating paper and in the insulating paper. As a result, the contact area and volume of partial discharge are changed, which further changes the characteristics of partial discharge for interturn insulation. This study can provide a reference for the maintenance and structural optimization of 750 kV transformers. [ABSTRACT FROM AUTHOR]

Introduction: The traditional partial discharge localization improvement strategy mainly starts from the intelligent algorithm, but fails to consider the influence of core winding and oil temperature on partial discharge positioning. Methods: This paper also considers the influence of the iron core winding and oil temperature. Through finite element simulation, a transformer model was established to analyze the propagation characteristics of ultrasonic signals generated by partial discharge under the interference of gradient oil temperature and winding. The chaotic firefly-particle swarm hybrid algorithm is proposed, and through the calculation of Shubert's multi-peak function. Finally, a partial discharge defect platform based on gradient oil temperature was built to verify the chaotic firefly-particle swarm hybrid localization algorithm. Results: The ultrasonic velocity generated by partial discharge in transformers cannot be fixed, and it is suggested that ultrasonic sensors should be installed near the center of the top of the transformer. The proposed algorithm can be better optimized in the case of multiple local extreme points. Under gradient oil temperature experiments, the algorithm achieves positioning errors less than 100 and 55 mm for cases with and without winding obstruction, respectively, with average positioning errors of 74.2 and 35.2 mm. Discussion: The positioning method in this paper can provide a technical reference for the partial discharge positioning of transformers in actual operation. [ABSTRACT FROM AUTHOR]

This work proposes a methodology for noise removal, separation, and classification of partial discharges in electrical system assets. Partial discharge analysis is an essential method for fault detection and evaluation of the operational conditions of high-voltage equipment. However, it faces several limitations in field measurements due to interference from radio signals, television transmissions, WiFi, corona signals, and multiple sources of partial discharges. To address these challenges, we propose the development of a clustering model to identify partial discharge sources and a classification model to identify the types of discharges. New features extracted from pulses are introduced to model the clustering and classification of discharge sources. The methodology is tested in the laboratory with controlled partial discharge sources, and field tests are conducted in substations to assess its practical applicability. The results of laboratory tests achieved an accuracy of 85% in classifying discharge sources. Field tests were performed in a substation of the Eletrobras group, allowing the identification of at least three potentially defective current transformers. [ABSTRACT FROM AUTHOR]

The reliable operation of vacuum circuit breakers (VCBs) plays an important role in the power system's reliability. The common cases which lead to the failure in the VCB are related to the loss of vacuum inside the vacuum interrupters (VIs). VIs commonly loses the vacuum with a higher leakage rate, resulting in a decrease in discharge voltage and partial discharge (PD) inside the VI which can lead to the eventual failure of the VCBs. Detecting vacuum leakage at the early stage became an important issue. In the prior study, vacuum failure was studied by detecting PD characteristics inside the VIs by regulating the different internal pressures. This study introduces a novel approach to investigating vacuum leakage within the VIs by detecting PD characteristics under various floating shield potentials to study the possibility of shield potential control on PD measurement. Comparing the experimental results measured at different internal pressures under various shield potentials, this study highlights the measurement of PD characteristics and the possibility of shield potential control on PD measurement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Partial discharge in oil paper insulation is an important reason for insulation breakdown. The study of partial discharge in transformer oil is of great significance for ensuring the reliability of transformers. In this paper, a comprehensive observation platform of partial discharge with needle plate electrode and oil-paper insulation is built, and the original shape image set of partial discharge is collected and established. Through multi-channel convolution processing of the true color image, gray level equivalent distribution of 3D section, segmentation stretching of threshold interval and binary neighborhood search, a general technical method suitable for processing a series of low brightness discharge images with background interference is finally obtained. [ABSTRACT FROM AUTHOR]

Under atmospheric pressure, partial discharge initiated by free metallic particles has consistently been a significant factor leading to failures in high-voltage electrical equipment. Simulating the propagation of negative streamer discharge in N2/O2 mixtures contributes to a better understanding of the occurrence and evolution of partial discharge, optimizing the insulation performance of electrical equipment. In this study, a two-dimensional plasma fluid dynamics model coupled with the current module was employed to simulate the evolution process of negative streamer discharge caused by one free metallic particle under a suspended potential at 220 kV applied voltage conditions. Simulation results indicated that the discharge process could be divided into two distinct stages: In the first stage, the electron ionization region detached from the electrode surface and propagated independently. During this stage, the corona discharge on the negative electrode surface provided seed electrons crucial for the subsequent development of negative corona discharge. The applied electric field played a dominant role in the propagation of the electron region, especially in the electron avalanche region. In the second stage, space charge gradually took over, causing distortion in the spatial field, particularly generating a substantial electric field gradient near the negative electrode surface, forming an ionization pattern dominated by ionization near the negative electrode surface. These simulation results contribute to a comprehensive understanding of the complex dynamic process of negative streamer discharge initiated by free metallic particles, providing essential insights for optimizing the design of electrical equipment and insulation systems. [ABSTRACT FROM AUTHOR]

Modern power grids are undergoing a significant transformation with the massive integration of renewable, decentralized, and electronically interfaced energy sources, alongside new digital and wireless communication technologies. This transition necessitates the widespread adoption of robust online diagnostic and monitoring tools. Sensors, known for their intuitive and smart capabilities, play a crucial role in efficient condition monitoring, aiding in the prediction of power outages and facilitating the digital twinning of power equipment. This review comprehensively analyzes various sensor technologies used for monitoring power transformers, focusing on the critical need for reliable and efficient fault detection. The study explores the application of fiber Bragg grating (FBG) sensors, optical fiber sensors, wireless sensing networks, chemical sensors, ultra-high-frequency (UHF) sensors, and piezoelectric sensors in detecting parameters such as partial discharges, core condition, temperature, and dissolved gases. Through an extensive literature review, the sensitivity, accuracy, and practical implementation challenges of these sensor technologies are evaluated. Significant advances in real-time monitoring capabilities and improved diagnostic precision are highlighted in the review. It also identifies key challenges such as environmental susceptibility and the long-term stability of sensors. By synthesizing the current research and methodologies, this paper provides valuable insights into the integration and optimization of sensor technologies for enhancing transformer condition monitoring and reliability in modern power systems. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is one of the major causes of insulation accidents in oil-immersed transformers, generating a large number of signals that represent the health status of the transformer. In particular, acoustic signals can be detected by sensors to locate the source of the partial discharge. However, the array, type, and quantity of sensors play a crucial role in the research on the localization of partial discharge sources within transformers. Hence, this paper proposes a novel sensor array for the specific localization of PD sources using COMSOL Multiphysics software 6.1 to establish a three-dimensional model of the oil-immersed transformer and the different defect types of two-dimensional models. "Electric-force-acoustic" multiphysics field simulations were conducted to model ultrasonic signals of different types of PD by setting up detection points to collect acoustic signals at different types and temperatures instead of physical sensors. Subsequently, simulated waveforms and acoustic spatial distribution maps were acquired in the software. These simulation results were then combined with the time difference of arrival (TDOA) algorithm to solve a system of equations, ultimately yielding the position of the discharge source. Calculated positions were compared with the actual positions using an error iterative algorithm method, with an average spatial error about 1.3 cm, which falls within an acceptable range for fault diagnosis in transformers, validating the accuracy of the proposed method. Therefore, the presented sensor array and computational localization method offer a reliable theoretical basis for fault diagnosis techniques in transformers. [ABSTRACT FROM AUTHOR]

The power transformer is one of the most crucial pieces of high-voltage equipment in the power system, and its stable operation is crucial to the reliability of power transmission. Partial discharge (PD) is a key factor leading to the degradation and failure of the insulation performance of power transformers. Therefore, online monitoring of partial discharge can not only obtain real-time information on the operating status of the equipment but also effectively predict the remaining service life of the transformer. Meanwhile, accurate localization of partial discharge sources can assist maintenance personnel in developing more precise and efficient maintenance plans, ensuring the stable operation of the power system. Dual partial discharge sources in transformer oil represent a more complex fault type, and piezoelectric transducers installed outside the transformer oil tank often fail to accurately capture such discharge waveforms. Additionally, the sensitivity of the built-in F-P sensors can decrease when installed deep within the oil tank due to the influence of oil pressure on its sensing diaphragm, resulting in an inability to accurately detect dual partial discharge sources in transformer oil. To address the impact of oil pressure on sensor sensitivity and achieve the detection of dual partial discharge sources under high-voltage conditions in transformers, this paper proposes an optical fiber ultrasonic sensor with a pressure-balancing structure. This sensor can adapt to changes in oil pressure environments inside transformers, has strong electromagnetic interference resistance, and can be installed deep within the oil tank to detect dual partial discharge sources. In this study, a dual PD detection system based on this sensor array is developed, employing a cross-positioning algorithm to achieve detection and localization of dual partial discharge sources in transformer oil. When applied to a 35 kV single-phase transformer for dual partial discharge source detection in different regions, the sensor array exhibits good sensitivity under high oil pressure conditions, enabling the detection and localization of dual partial discharge sources in oil and winding interturn without obstruction. For fault regions with obstructions, such as within the oil channel of the transformer winding, the sensor exhibits the capability to detect the discharge waveform stemming from dual partial discharge sources. Overall, the sensor demonstrates good sensitivity and directional clarity, providing effective detection of dual PD sources generated inside transformers. [ABSTRACT FROM AUTHOR]

Featured Application: Development of intelligent and real-time monitoring systems for transformer health diagnostics and condition monitoring. Power transformers, like other High-Voltage (HV) electrical equipment, experience aging and insulation degradation due to chemical, mechanical and electrical forces during their operation. Partial discharges (PD) are among the most predominant insulation breakdown mechanisms. Monitoring partial discharges has proven to provide valuable information on the state of the insulation systems of power transformer, allowing transformer operators to make calculated decisions for maintenance, major interventions and plan for replacement. This systematic literature review aims to systematically examine the use of machine learning techniques in classifying PD in transformers to present a complete indicator of the available literature as well as potential literature gaps which will allow for future research in the field. The systematic review surveyed a total of 81 research literatures published from 2010 to 2023 that fulfilled a specific methodology which was developed as part of this study. The results revealed that supervised learning has been the most widely used Artificial Intelligence (AI) algorithm, primarily in the form of Support Vector Machine (SVM). The collected research indicated 20 countries represented in the publications, with China carrying out 32% of the research, followed by India with 10%. Regarding PD, the survey revealed that most researchers tend to investigate numerous types of PD and compare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence of PD is widely used versus the use of actual power transformers. Most of the literature tends to not specify the physical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) fault diagnosis is of great importance for ensuring the safe and stable operation of power transformers. To address the issues of low accuracy in traditional PD fault diagnostic methods, this paper proposes a novel method for the power transformer PD fault diagnosis. It incorporates the approximate entropy (ApEn) of symplectic geometry mode decomposition (SGMD) into the optimized bidirectional long short-term memory (BILSTM) neural network. This method extracts dominant PD features employing SGMD and ApEn. Meanwhile, it improves the diagnostic accuracy with the optimized BILSTM by introducing the golden jackal optimization (GJO). Simulation studies evaluate the performance of FFT, EMD, VMD, and SGMD. The results show that SGMD–ApEn outperforms other methods in extracting dominant PD features. Experimental results verify the effectiveness and superiority of the proposed method by comparing different traditional methods. The proposed method improves PD fault recognition accuracy and provides a diagnostic rate of 98.6%, with lower noise sensitivity. [ABSTRACT FROM AUTHOR]

Quality control is highly relevant for safety, sustainability and efficiency of the battery manufacturing process. An early and reliable detection of failures in the production chain is important. Here we present a method for detecting micrometric imperfections and contaminations on the battery separator before filling the battery stack with the electrolyte. We sense these irregularities by measuring an increase of partial discharges when applying between the battery electrodes potentials close, but still well below the breakdown voltage of the separator. We can distinguish different degrees and different types of contamination with a very high confidence. This is enabled by a throughout statistical analysis of the partial discharge events. The overall reliability of detecting a contaminated against the clean separator is 96 %. The technique, as implemented here, uses categorization procedures and machine learning algorithms to automate decision‐making and can accelerate the quality assessment process in pilot lines or small‐ manufacturing. Compared to other methods, like optical detection or full discharge measurements, the here presented approach is very reliable, simple to implement and virtually noninvasive. [ABSTRACT FROM AUTHOR]

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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.)

Partial discharge (PD) is a crucial and intricate electrical occurrence observed in various types of electrical equipment. Identifying and characterizing PDs is essential for upholding the integrity and reliability of electrical assets. This paper proposes an ensemble methodology aiming to strike a balance between the model complexity and the predictive performance in PD pattern recognition. A simple convolutional neural network (SCNN) was constructed to efficiently decrease the model parameters (quantities). A quadratic support vector machine (QSVM) was established and ensembled with the SCNN model to effectively improve the PD recognition accuracy. The input for QSVM consisted of the circular local binary pattern (CLBP) extracted from the enhanced image. A testing prototype with three types of PD was constructed and 3D phase-resolved pulse sequence (PRPS) spectrograms were measured and recorded by ultra-high frequency (UHF) sensors. The proposed methodology was compared with three existing lightweight CNNs. The experiment results from the collected dataset emphasize the benefits of the proposed method, showcasing its advantages in high recognition accuracy and relatively few mode parameters, thereby rendering it more suitable for PD pattern recognition on resource-constrained devices. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications. [ABSTRACT FROM AUTHOR]

To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm–Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects. [ABSTRACT FROM AUTHOR]

In answer to the demand for high sensitivity and miniaturization of ultra-high frequency (UHF) sensors for partial discharge (PD) detection in power equipment, this paper proposes research on miniaturized UHF-sensing technology for PD detection in power equipment based on symmetric cut theory. The symmetric cut theory is applied for the first time to the miniaturization of PD UHF sensors for power equipment. A planar monopole UHF sensor with a size of only 70 mm × 70 mm × 1.6 mm is developed using an exponential asymptotic feed line approach, which is a 50% size reduction. The frequency–response characteristics of the sensor are simulated, optimized and tested; the results show that the standing wave ratio of the sensor developed in this paper is less than 2 in the frequency band from 427 MHz to 1.54 GHz, and less than 5 in the frequency band from 300 MHz to 1.95 GHz; in the 300 MHz~1.5 GHz band; the maximum and average gains of the sensor E-plane are 4.76 dB and 1.02 dB, respectively. Finally, the PD simulation experiment platform for power equipment is built to test the sensor's sensing performance; the results show that the sensor can effectively detect the PD signals; the sensing sensitivity is improved by about 95% relative to an elliptical monopole UHF sensor. [ABSTRACT FROM AUTHOR]

Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals. [ABSTRACT FROM AUTHOR]

The increase in the global temperature due to greenhouse gas emissions is a major concern to the world. To achieve the goal of zero emissions by 2050 in the USA the practical realization of all-electric vehicles, particularly all-electric aircraft (AEA), is important. For the design of electrical power systems (EPSs) in all-electric aircraft, a bipolar medium-voltage direct current (MVDC) system of ±5 kV is being investigated. However, several challenges manifest when using such voltages in a low-pressure environment. One of the main challenges is the partial discharge (PD) behavior of the insulation. It is important to study the PD behavior of the insulation by simulating the aviation environment in the lab. This work aimed to study the partial discharge behavior of air under a negative DC voltage in a needle-to-plane electrode geometry by simulating the aviation pressures in the lab. The partial discharge inception voltage (PDIV) and the breakdown voltage (BDV) show an obvious pressure-dependent variation. Regression analysis was performed to better understand the relationship between the PDIV and pressures. Plots were drawn for the average discharge current at each voltage step until breakdown. This paper's findings can provide valuable insight into the design of EPS for an AEA. To the best of our knowledge, such a study has not been carried out to date. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) diagnosis is essential for assessing the insulation status of power equipment, but onsite interferences often contaminate PD signals with noise, impacting diagnostic accuracy. This work proposes an adaptive wavelet threshold denoising technique, where the PD signal is first decomposed into wavelet coefficients using discrete wavelet transform (DWT). Traditional threshold selection methods rely on experience and statistical factors, challenging optimal threshold determination. To address this issue, Particle Swarm Optimization (PSO), Energy Valley Optimization (EVO) and Subtraction Average Based Optimization (SABO) are applied to achieve the best adaptive threshold. The proposed method is evaluated against traditional sqtwolog-based threshold methods using root mean square error (RMSE) and the recognition accuracy of classifiers, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and K-Nearest Neighbours (KNN). The results show that the proposed technique can find the best threshold and increase the recognition accuracy by 19% compared to the traditional method, demonstrating its superior performance.

During the operation of oil-immersed current transformers, they gradually deteriorate due to various factors. In order to study the relationship between partial discharge and gas generation characteristics under different typical defects, this paper measures the unit-time discharge energy, unit-time apparent discharge quantity, pulse repetition rate, and gas generation rate during the discharge development process of three typical defects: protrusion defect, surface defect, and gas gap defect. The study reveals that the generation of hydrocarbon gases and hydrogen in the oil mainly depends on discharges with an apparent discharge quantity above 300 pC. Among the discharge characteristics, the unit-time discharge energy and unit-time average discharge quantity significantly impact the gas generation rate. It is found that the content of C2H2 and C2H4 in the oil for protrusion and surface defects increases significantly with discharge intensity, while CH4 and H2 show significant changes for gas gap defects. Additionally, an approximately linear relationship exists between discharge energy per unit time, pulse repetition rate, and the rate of characteristic gas generation for both protrusion and surface defects, as well as for gas gap defects. These findings provide a reference for diagnosing current transformers and highlight the critical role of discharge characteristics in gas generation dynamics.

Drying of transformer winding insulation has a direct impact on the dependability and durability of the transformer. The extraction of moisture from paper insulation is a crucial need in the manufacturing process of transformers. Insulation efficiency in transformers can be reduced over time due to the detrimental effects of temperature, moisture, and air, often known as aging. Heat, oxygen, and residual moisture increase the deterioration of cellulose in transformer solid insulation. This might potentially result in premature failure of the transformer. The factory's insulation drying process should ensure that the residual moisture content of bulk solid insulation remains below 0.5 %. The primary aim is to maintain a high degree of polymerization, often ranging from 1000 to 800. This article investigates the factors contributing to the degradation of cellulose insulation and analyzes various techniques that are used in manufacturing plants in Kazakhstan. In addition, this study experimentally investigates the impact of vacuum on the process of moisture evaporation and partial discharge (PD) in analysis with other methods of drying transformers.

Ultra-high frequency (UHF) antenna-sensors are becoming popular for non-invasive detection of unwanted electric discharge i.e., partial discharge (PD) in high-voltage (HV) systems. Early PD signals are weak for detection and classification by UHF antenna-sensors. Early PD detection and distinction are crucial to prevent equipment failure. Typically, PD signals are distinguished in HVAC by phase resolved PD (PRPD) patterns. Whereas in HVDC, some unconventional methods are applied. However, a blind distinction of PDs under unknown HVAC/HVDC conditions still remains a challenge. In this article, we address this issue by using a lotus-shaped UHF antenna-sensor for early PD detection. Unlike cut-and-try technique-based conventionally designed bio-inspired antennas, our sensor is designed by a precisely derived equivalent circuit model to systematically optimize antenna gain over size to detect early PDs. The fabricated sensor has a size of 18×11 cm2, an average realized gain of 3.05 dBi in 740–1600 MHz frequencies, and a sensitivity index of 154.04 dBi/m2. The sensor prototype is applied to wirelessly distinguish PD signals from unknown sources under HVAC and HVDC by pulse sequence analysis. Early detection, characterization, and distinction of unknown PD signals are ensured by the proposed sensor and interpretation technique. This work offers a distinctive PD sensing method for HVAC/HVDC converter stations.

Electrical machines are usually operated in a very harsh environment, therefore great attention has to be given towards the designing of insulating materials and insulation systems. The severe operating environment invites corrosion, humidity, high temperature, and so on. This article is a review of electrical machine’s insulation design, techniques, and methodologies for modeling and testing the insulation materials and the recent advancements in this field. Several testing standards and methods to detect insulation failure have been discussed. Case studies of insulation failure in the electrical machine have been discussed to draw the reader’s attention towards a more realistic approach. Issues related to high voltage insulation systems used in industries like aerospace electric powertrain, hydro generators and, wind turbine generators have been briefed in the article. Partial discharge monitoring techniques are explored in this article. Its consequences on most emerging and advanced high voltage, high-altitude aerospace applications, will be discussed. Finally, polymer nanocomposite materials with exceptional dielectric strength or thermal conductivity are underlined as an outlook for future consideration in machine insulation design.