A new approach to estimate the direction-of-arrival (DOA) in a Time-Modulated Array (TMA) system with a bipolar squared periodic time-modulating sequence is proposed in this letter. Using such a sequence can increase the power of harmonic components of the received signal. Based on this fact, we formulated a new parameter pertaining to the ratio of the upper and lower sideband first harmonic components of the received signal, to improve the accuracy of the DOA estimation in a low signal-to-noise ratio (SNR) environment. A numerical analysis was performed to verify the effectiveness of the proposed DOA estimation method.

In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods.

Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively.

In this paper, the amount of SF₆ decomposition gases due to the partial discharge (PD) was studied in the SF₆ gas-insulated transformer. The long-term PD degradation experiment was performed while controlling the discharge magnitude using the surface discharge, and the gas generation amount was measured by using gas chromatography for SO₂F₂, SOF₂, SO₂, CO, and CF₄. In addition, to investigate the relationship between the partial discharge energy and the decomposed gas generation amount, partial discharge energy was calculated by a data processing program and converted to the unit of joule per mole. With the finite element method (FEM), the electric field distribution and SF₆ gas decomposition mechanism were explained for the partial discharge energy effect on the gas generation. This study helps understand the relationship between the partial discharge energy and the decomposed gas generation ratio with the experimental results and can be used for the diagnosis of PD and maintenance process for the gas-insulated transformers.

Recently, deep neural networks have shown remarkable success in fault diagnosis in power systems using partial discharges (PDs), thereby enhancing grid asset safety and reliability. However, the prevailing approaches often adopt centralized large-scale datasets for training, without taking into account the impact of noise environments for Intelligent Electronic Devices (IEDs). Noise environments for PD measurements in gas-insulated switchgear (GIS) introduce variations in feature distributions and class representations, challenging the generalization ability of the trained models in new and diverse conditions. In this study, we propose a Shared Knowledge-based Contrastive Federated Learning (SK-CFL) for PD diagnosis in different noise environments for IEDs. The proposed SK-CFL combines federated learning principles with contrastive learning, empowering IEDs to collaboratively learn and share knowledge as regards PD and noise patterns. The proposed framework can learn representations between the same patterns across different IEDs while ensuring data privacy. Experimental results for PD diagnosis in GIS show that the proposed SK-CFL achieves a performance improvement in fault diagnosis, particularly in new and unseen environments. Specifically, the recall for unknown noise in untrained IED 6 demonstrates 92.86% of the proposed SK-CFL, in comparison with 64.29% and 35.71% of the conventional FL and baseline method, respectively. These results suggest that the proposed SK-CFL approach promises more adaptable, and resilient data-driven approaches since it protects data privacy that can operate effectively in challenging real-world environments.

Effective monitoring and diagnosis of partial discharge (PD) in power equipment is crucial for maintenance, particularly given the expectations of significant increases in energy generation and consumption. Although deep neural networks have been widely applied in PD fault detection and classification, their performance suffers from insufficient labeled data in power equipment. This study proposes semi-supervised learning (SSL) to address the scarcity of labeled training data for PD classification in gas-insulated switchgear (GIS). The proposed SSL was validated based on phase-resolved PD and on-site noise using an ultra-high frequency (UHF) PD measurement system. Experimental results show that the proposed SSL achieves a high classification accuracy of 94.59% by effectively utilizing unlabeled data to enhance classification performance in GIS.