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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]

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]

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]

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]

• Polymer insulators are susceptible to damage and accelerated aging due to contaminants, impacting their performance and structural integrity. • This study conducts comprehensive investigations about discharge behaviors of polymer insulator when exposed to three distinct contaminants: fly ash, salt, and algae. • Measurements of inception and surface breakdown voltages, along with partial discharge patterns, reveal unique responses to each contaminant. Salt intensifies electrical stress under dry conditions, accelerating aging and increasing partial discharge compared to other contaminants. Fly ash alters Partial Discharge Inception Voltage and Surface Breakdown Voltage, enhancing surface discharge performance, while algae's biofouling process deteriorates insulator surfaces, decreasing PDIV and altering PD patterns. • The findings of this study contribute to the enrichment of references related to partial discharge (PD) phenomena caused by salt, fly ash, and algae contaminants. By identifying unique PD patterns associated with each contaminant, this research enhances the ability to diagnose and mitigate insulator degradation in diverse environmental conditions. The performance of polymer insulators can be significantly compromised by the presence of contaminants, resulting in damage, structural alterations, and accelerated aging. Hence, this research aimed to investigate the discharge behavior of polymer insulators when exposed to various types of contaminants. Three distinct contaminants, namely fly ash, salt, and algae, were prepared for the study. In order to assess the characteristics of these insulators, measurements of inception and surface breakdown voltages were conducted for each sample. Additionally, partial discharge measurements were performed at two different applied voltages to observe the patterns of partial discharge under different contaminants. To aid in distinguishing the characteristics of partial discharge, a statistical method based on graphical analysis was employed. Furthermore, visual inspection was carried out after the occurrence of partial discharge activity to provide an explanation for these phenomena. The findings of the study revealed that each contaminant produced a unique pattern, which was influenced by the specific elements present in the respective contaminant. It was observed that contaminants exhibit varying effects on partial discharge depending on their nature. Salt was found to induce faster stress propagation, fly ash inhibited stress propagation, and algae had a degrading effect on the surface of the polymer insulators. [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]