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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. [Display omitted] [ABSTRACT FROM AUTHOR]

This work deals with an investigation of negative corona discharge generated from standing wire particles in air insulation. Eight samples from different particle lengths and diameters were studied to understand the effect of particle geometries on discharge characteristics. The particle was stood artificially on the ground inside parallel plane electrodes and was energized with a positive HVDC generator. Pulse sequence analysis (PSA) technique was used to evaluate the corona discharge patterns and behaviors. Under the same applied voltage, it is concluded that an increase in particle length causes a rising trend on the average discharge current and pulse magnitude, as well as the pulses appear more frequent and fluctuate. Meanwhile, variation of the particle diameter shows that a larger particle size generates fewer pulses and a lower average discharge current. However, the pulse magnitude is higher and appears randomly with larger particle diameter. A simulation model based on Finite Element Method (FEM) was also established to explain the behaviors better. It is found that the pulse repetition rate is related to the electric field distribution and negative ions movement, while the pulse magnitude is related to ionization rate and positive ions velocity. [ABSTRACT FROM AUTHOR]

Monitoring the partial discharge (PD) activity of power equipment insulation is crucial to ensure uninterrupted power system operation. PD occurrence is highly correlated to weakened insulation strength. If PD occurrences are left unchecked, unexpected insulation breakdowns may occur. The comprehensive PD diagnostic process includes the detection, localization, and classification of PD. Accurate PD source localization is necessary to locate the weakened insulation segment. As a result, rapid and precise PD localization has become the primary focus of PD diagnosis for power equipment insulation. This paper presents a review of different approaches to PD localization, including conventional, machine learning (ML), and deep learning (DL) as a subset of ML approaches. The review focuses on the ML and DL approaches developed in the past five years, which have shown promising results over conventional approaches. Additionally, PD detection using conventional, unconventional, and a PCB antenna designed based on UHF techniques is presented and discussed. Important benchmarks, such as the sensors used, algorithms employed, algorithms compared, and performances, are summarized in detail. Finally, the suitability of different localization techniques for different power equipment applications is discussed based on their strengths and limitations. [ABSTRACT FROM AUTHOR]

In this article, we have performed a comprehensive investigation in the specialty of ultra-high frequency (UHF) printable antennas for partial discharge (PD) diagnostics. Due to the complex characteristics of PD signals in UHF range, selection of printable antenna becomes a major concern for PD diagnostics. The main contribution of this article is that we have provided a complete roadmap of selecting UHF printable antennas through relating antenna parameters with their practical significance on PD diagnostics in high-voltage equipment. We have highlighted 7 intrinsic parameters of UHF antennas and indicated their relationship with 7 extrinsic parameters of PD diagnostics performance. Following the extracted relationship, we have investigated on 8 different designing methods of UHF antennas. Based on the available results of recently reported works and their critical analyses, we have pointed out the existing challenges and indicated the future direction of UHF printable antennas for contactless partial discharge diagnostic systems. [ABSTRACT FROM AUTHOR]

The aim of this article is to present a review of the development and application of superhydrophobic insulation for outdoor high-voltage applications. The methods of preparing insulation exhibiting superhydrophobic properties to mitigate the pollution problem under high voltages in the outdoors are explored. In addition, the existing evaluation techniques at the material development stage are reviewed and potential new modifications are discussed. The self-cleaning and anti-icing properties, the electrical performance, and prospects of superhydrophobic insulators are also addressed. Although superhydrophobic insulators may show promising behavior in small-scale laboratory conditions that do not use electrical stress, the effect of surface micro–nano structure responsible for the superhydrophobicity on the performance under the actual outdoor and weathering conditions under voltage remains to be fully understood and verified. Therefore, there is a need to verify the feasibility of superhydrophobic insulators for outdoor high-voltage applications with a solid evaluation protocol, while using material screening methods in the laboratory, and with reference to field experience. [ABSTRACT FROM AUTHOR]

In high-voltage equipment insulation, multiple partial discharge (PD) sources may exist at the same time. Therefore, it is important to identify PDs from different PD sources under noisy condition in insulations, with the highest accuracy. Although many studies on classifying different PD types in insulation have been performed, some signal processing methods have not been used in the past for this application. Thus, in this work, Cepstrum analysis on PD signals combined with artificial neural network (ANN) is proposed to classify the PD types from different PD sources simultaneously under noisy condition. Measurement data from different sources of artificial PD signals were recorded from insulation materials. Feature extractions were performed on the recorded signals, including Cepstrum analysis, discrete wavelet transform, discrete Fourier transform, and wavelet packet transform for comparison between the different methods. The features extracted were used to train the ANN. To investigate the classification accuracy under noisy signals, the remaining data were corrupted with artificial noise. The noisy data were classified using the ANN, which had been trained by noise-free PD signals. It is found that Cepstrum-ANN yields the highest classification accuracy for noisy PD signals than the other methods tested. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Cable joints are the weakest point in cross-linked polyethylene (XLPE) cables and are susceptible to insulation failures. Partial discharge (PD) analysis is a vital tool for assessing the insulation quality in cable joints. Although many works have been done on PD pattern classification, it is usually performed in a noise-free environment. Also, works on PD pattern classification are mostly done on lab fabricated insulators, where works on actual cable joint defects are less likely to be found in literature. Therefore, in this work, classifications of cable joint defect types from partial discharge measurement under noisy environment were performed. Five XLPE cable joints with artificially created defects were prepared based on the defects commonly encountered on-site. A high noise tolerance principal component analysis (PCA)-based feature extraction was proposed and compared against conventional input features such as statistical and fractal features. These input features were used to train the classifiers to classify each defect type in the cable joint samples. Classifications were performed using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). It was found that the proposed PCA features displayed the highest noise tolerance with the least performance degradation compared to other input features under noisy environment. [ABSTRACT FROM AUTHOR]

• This study uses partial discharge data measured from cable joints as the dataset. • Partial discharge data was used to train 4 convolutional neural networks. • The system was trained with clean data but tested with data overlapped with noise. • Novel data augmentation was proposed to improve performance under noisy condition. • Proposed data augmentation improved classification performance by 15.83–29.05%. Insulation fault diagnosis is essential because faulty insulation will lead to the interruption of power delivery. Partial discharge (PD) pattern classification is widely used in insulation diagnosis to identify incipient fault before catastrophic failure occurs. PD classification systems improved significantly due to the emergence of advanced machine learning techniques such as deep learning algorithms. However, classification systems trained in ideal noise-free condition suffer severe performance degradation when tested on-site where the condition is nonideal due to the presence of noise contamination. In this work, a novel data augmentation technique was implemented where simulated noise was added to create augmented data. The augmented data was used in tandem with ideal condition PD data during the training phase of four well known convolutional neural networks (CNN). For a realistic performance evaluation, the classification system was trained with ideal noise-free data and augmented data but tested with data overlapped with actual measured noise representing nonideal condition via K-fold cross-validation. The results showed that the proposed data augmentation technique improved the performance of CNN under nonideal condition by 15.83%–29.05% without compromising its performance under ideal condition. [ABSTRACT FROM AUTHOR]

In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault. [ABSTRACT FROM AUTHOR]

It is well known that a correlation exist between the pattern of partial discharge (PD) behavior and the insulation quality. Since different sources of partial discharge have their own unique effects on the degradation of insulation material, it is vital to investigate the relationship between the defect type and the PD to determine the insulation quality. Numerous work had been done to classify partial discharge patterns with variable success. Past research work in partial discharge classification varies greatly in terms of classification techniques used, choice of feature extraction, denoising method, training process, artificial defects created for training purposes and performance assessment. Therefore it is necessary for a literature survey to access the state of the art development in partial discharge classification. [ABSTRACT FROM AUTHOR]

There are many factors, which influence the behaviours of partial discharge (PD) within a void located in a solid dielectric material. One of the important factors is the waveform of the applied voltage on the electrode of a system, such as its amplitude and frequency. In this study, a two‐dimensional axial‐symmetric model of a cylindrical void in polyethylene layers has been developed using finite element analysis software. The model was used to simulate PD activity in the void under square waveform applied voltage under different amplitudes of the voltage. The model has considered the effect of charge propagation along the void surface during a PD occurrence. The obtained simulation results were compared with the measurement results reported from previous literature to validate the PD model that has been developed in this work. It was found that the simulation and measurement results are within reasonable agreement with only slight disagreement. From the comparison, critical parameters from the model that affect PD behaviours under square waveform applied voltage were found to be the electron generation rate and the inception, extinction and critical charge propagation fields. These findings may increase an understanding of PD behaviours within a void in a dielectric material under square waveform applied voltage, which is important for insulation diagnosis. [ABSTRACT FROM AUTHOR]

This study provides a thorough investigation of partial discharge (PD) activities in nanofluid insulation material consisting of different types of nanoparticles, which are conductive and semiconductive when subjected to high voltage stress is presented. Nanofluids have become a topic of interest because they can be an alternative to liquid insulation in electrical apparatus due to their promising dielectric strength and cooling ability. However, during in-service operation, PDs can occur between conductors in the insulation system. Therefore, this study presents the behavior of PDs within nanofluid dielectric materials consisting of conductive and semiconductive nanoparticles. The results show that there is an improvement in the PD resistance and a reduction in the tan delta of nanofluids at power frequency after the incorporation of conductive or semiconductive nanoparticles in the nanofluid oil. However, the most suitable concentration of conductive and semiconductive nanoparticles in the base fluid was found to be, respectively, 0.01 g/L and 1.0 g/L at PD inception and PD steady-state conditions. The clustering of nanoparticles in a nanofluid suspension due to PD activities is also discussed in this study. [ABSTRACT FROM AUTHOR]

Modeling of the partial discharge (PD) process allows a better understanding of the phenomena. In this paper, a simulation model for spherical cavities within a homogeneous dielectric material has been developed. The model is implemented using Finite Element Analysis (FEA) software in parallel with a mathematical package. This method provides many advantages over previous PD models because discharge events can be simulated dynamically and the electric field in the cavity can be calculated numerically. The model has been used to study the effect of different amplitudes and frequencies of the applied voltage and simulation results have been compared with experimental measurement results. It is found that certain model parameters are dependent on the applied stress and parameters that clearly affect PD activity can be readily identified, these parameters include; the electron detrapping time constant, the cavity surface conductivity, the initial electron generation rate and the extinction voltage. The influence of surface charge decay through conduction along the cavity wall on PD activity has also been studied. [ABSTRACT FROM AUTHOR]

• This study uses real partial discharge measured from cable joints as the dataset. • Partial discharge data was used as an image to train a convolutional neural network. • 5 networks & 4 image types were tested to find the best noise invariant classifier. • The system was trained using clean data but tested using data overlapped with noise. • The proposed system has 16.90% better performance than traditional methods. Partial discharge (PD) pattern recognition is essential since it can help to identify the nature of the insulation defect. Numerous machine learning models have been utilized for PD classification applications in the past. However, traditional machine learning models rely on manual feature extraction to obtain training data. They are usually trained using clean PD data measured in the laboratory but are expected to work on-site where some degree of interference or noise is expected. When tested using clean PD data, most machine learning models can easily achieve above 90% accuracy. However, when tested using PD data overlapped with noise, classification accuracy reduces significantly. In this work, the development of a convolutional neural network (CNN)-based PD classification system using transfer learning was proposed. In order to achieve a more practical performance evaluation, a modified 10-fold cross-validation procedure was used where the CNN-based PD classifier was trained using clean PD data but tested using PD data that has been overlapped by noise. The results showed that CNN-based PD classifier was able to achieve up to 16.90% higher classification accuracy under noise contamination compared to traditional machine learning with manual feature extraction. This shows that the proposed method was able to retain higher classification accuracy in the presence of noise. [ABSTRACT FROM AUTHOR]