Discharge Detection Research Articles

Partial Discharge Detection Method of Electrical Equipment Based on UHF Method

Partial discharge is an early symptom of insulation defects. In the field application, the detection method can not accurately identify those noise signals mixed in the partial discharge signal, which limits the detection to a certain extent. Based on UHF method, a partial discharge detection method for electrical equipment is designed. When partial discharge occurs, the exchange between charges, the radiation of electromagnetic waves and the consumption of energy will occur at the discharge position, causing the change of electrode potential. For the phase distribution spectrum, the signal characteristics produced by partial discharge are extracted from three aspects: statistical information, time domain information and frequency domain information. During detection, UHF sensor is placed on the basin insulator, and then UHF signal is received. Insulation defects are identified according to various differential discharge patterns formed by the discharge characteristics of different defects. Test results show that the signal spectrum detected by UHF partial discharge roughly matches the typical discharge spectrum, and it is determined that there is partial discharge in this area. This method has high positioning accuracy and meets the detection requirements of electrical equipment.

Deep learning and data augmentation for partial discharge detection in electrical machines

Fault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair case study. As a result, the two-dimensional convolutional neural network with continuous wavelet transform achieves the best accuracy of around 99.76% on a test dataset of PD signals originating from previously not utilized test objects. All DL models considered in this comparison outperform the state-of-the-art threshold-based PD classification. Even for PD events with an amplitude close to the noise level, the detection rate is still around 95% for the best network. Furthermore, without applying the proposed data augmentation procedure, the DL models investigated are not able to distinguish small PD pulses from noise.

Ecologically mediated differences in electric organ discharge drive evolution in a sodium channel gene in South American electric fishes.

Active electroreception-the ability to detect objects and communicate with conspecifics via the detection and generation of electric organ discharges (EODs)-has evolved convergently in several fish lineages. South American electric fishes (Gymnotiformes) are a highly species-rich group, possibly in part due to evolution of an electric organ (EO) that can produce diverse EODs. Neofunctionalization of a voltage-gated sodium channel gene accompanied the evolution of electrogenic tissue from muscle and resulted in a novel gene (scn4aa) uniquely expressed in the EO. Here, we investigate the link between variation in scn4aa and differences in EOD waveform. We combine gymnotiform scn4aa sequences encoding the C-terminus of the Nav1.4a protein, with biogeographic data and EOD recordings to test whether physiological transitions among EOD types accompany differential selection pressures on scn4aa. We found positive selection on scn4aa coincided with shifts in EOD types. Species that evolved in the absence of predators, which likely selected for reduced EOD complexity, exhibited increased scn4aa evolutionary rates. We model mutations in the protein that may underlie changes in protein function and discuss our findings in the context of gymnotiform signalling ecology. Together, this work sheds light on the selective forces underpinning major evolutionary transitions in electric signal production.

Development of new pocket distribution network inspection equipment

This paper aims to explore a new intelligent inspection mode of pocket distribution network equipment, which realizes rapid and comprehensive inspection and inspection by using terminal +N intelligent sensors and partial discharge detection and infrared temperature measurement technology as the core. First, this paper introduces the background and motivation of the new mode, and emphasizes the limitations of the existing traditional inspection methods and the problems that need to be improved. Then, the design and manufacture of pocket devices are described in detail, including key aspects such as sensor integration, portability and user-friendliness. Finally, the paper summarizes the research results and emphasizes the potential benefits and future research directions of this new intelligent inspection model.

Compact Archimedean spiral antenna with high gain for electrostatic discharge detection

AbstractA compact Archimedean spiral antenna with high gain for electrostatic discharge (ESD) detection is investigated. A frustum‐shaped cavity proposed in this work is formed to optimise the maximum antenna gain. Additionally, a helix arm is loaded to enhance antenna gain in lower band. The measured results demonstrate that the proposed antenna, which employs a unidirectional radiation mode, exhibits a maximum antenna gain ranging from −0.74 dBi to 9.46 dBi and a voltage standing wave ratio less than 3 across a frequency range of 0.65–5 GHz. A test system consisted of proposed antenna and a commercial ESD detection antenna is established. The verified results indicate that the proposed antenna has a performance close to the commercial antenna, so that proposed antenna with compact size is preferred to be utilised for ESD detection.

Semiautomated electric source imaging determines epileptogenicity of encephaloceles in temporal lobe epilepsy.

We aimed to assess the ability of semiautomated electric source imaging (ESI) from long-term video-electroencephalographic (EEG) monitoring (LTM) to determine the epileptogenicity of temporopolar encephaloceles (TEs) in patients with temporal lobe epilepsy. We conducted a retrospective study involving 32 temporal lobe epilepsy patients with TEs as potentially epileptogenic lesions in structural magnetic resonance imaging scans. Findings were validated through invasive intracerebral stereo-EEG in six of 32 patients and postsurgical outcome after tailored resection of the TE in 17 of 32 patients. LTM (mean duration = 6 days) was performed using the 10/20 system with additional T1/T2 for all patients and sphenoidal electrodes in 23 of 32 patients. Semiautomated detection and clustering of interictal epileptiform discharges (IEDs) were carried out to create IED types. ESI was performed on the averages of the two most frequent IED types per patient, utilizing individual head models, and two independent inverse methods (sLORETA [standardized low-resolution brain electromagnetic tomography], MUSIC [multiple signal classification]). ESI maxima concordance and propagation in spatial relation to TEs were quantified for sources with good signal quality (signal-to-noise ratio > 2, explained signal > 60%). ESI maxima correctly colocalized with a TE in 20 of 32 patients (62.5%) either at the onset or half-rising flank of at least one IED type per patient. ESI maxima showed propagation from the temporal pole to other temporal or extratemporal regions in 14 of 32 patients (44%), confirming propagation originating in the area of the TE. The findings from both inverse methods validated each other in 14 of 20 patients (70%), and sphenoidal electrodes exhibited the highest signal amplitudes in 17 of 23 patients (74%). The concordance of ESI with the TE predicted a seizure-free postsurgical outcome (Engel I vs. >I) with a diagnostic odds ratio of 2.1. Semiautomated ESI from LTM often successfully identifies the epileptogenicity of TEs and the IED onset zone within the area of the TEs. Additionally, it shows potential predictive power for postsurgical outcomes in these patients.

Advanced Preliminary Screening for PD Detection for Overhead Distribution Lines with Covered Conductors

This study proposes a novel approach to optimize partial discharge detection in overhead lines with covered conductors. The approach uses 2D Convolutional Neural Networks and HW neural network accelerator Edge TPU. The critical advancement of this research lies in exploring two distinct 2D classification methods - 2D histograms and spectrogram - and adjusting neural network thresholds to selectively identify potential positive PD samples with enhanced accuracy. The proposed approach addresses the prevalent challenge of costly and limited remote data transmission in PD detection. It significantly reduces the need for extensive data transfer by focusing on potentially positive samples. This evaluation of two approaches for detection of partial discharges, coupled with the strategic threshold adjustment, presents a novel solution in the realm of PD detection, offering increased efficiency and cost-effectiveness. By evaluating and comparing these strategies of 2D classification and threshold optimization, this research contributes to the field of partial discharges detection. It proposes a method that not only minimizes operational costs but also aligns with environmental sustainability goals, paving the way for more advanced maintenance practices in power transmission systems.

Surface Discharge Detection and Severity Assessment of Printed Circuit Boards for Electric Aircraft Using Fluorescent Fiber

Surface Discharge Detection and Severity Assessment of Printed Circuit Boards for Electric Aircraft Using Fluorescent Fiber

Partial discharge detection method for power equipment based on UHF method

Partial discharge detection method for power equipment based on UHF method

Information Timeliness Aware Multispectral Integrated Sensing, Communication, and Computing for High-Voltage Discharge Detection

Information Timeliness Aware Multispectral Integrated Sensing, Communication, and Computing for High-Voltage Discharge Detection

Recurrence Plot Aided Partial Discharge Detection Framework Employing HFCT Sensor and Customized Convolutional Neural Network

Recurrence Plot Aided Partial Discharge Detection Framework Employing HFCT Sensor and Customized Convolutional Neural Network

Partial Discharge Detection Method for Power Equipment Based on UHF Method

Partial Discharge Detection Method for Power Equipment Based on UHF Method

Optical Partial Discharge Detection and Diagnosis Method Based on PHOG Features

Optical Partial Discharge Detection and Diagnosis Method Based on PHOG Features

Multi-source partial discharge pattern recognition algorithm based on DCGAN-Yolov5

This study aims to overcome the complication that deep learning-based pattern recognition in partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) can only identify single-source partial discharge but not multi-source partial discharge. Specifically, a GIS multi-source partial discharge detection algorithm based on Deep Convolution Generative Adversarial Networks and You Only Look Once (DCGAN-YOLOv5) is proposed. First, the Phase Resolved Partial Discharge (PRPD) features of multi-source PD are analyzed, a GIS experiment platform is established, and four typical PD defects are simulated. Besides, sample data are collected, and the DCGAN network is used for sample expansion. Then, a YOLOv5 network model is designed, and a spatial and channel attention mechanism is added to the feature extraction network with a positive sample equilibrium strategy. Finally, the effectiveness of the proposed algorithm is verified using laboratory data and field data collected from a 220 kV substation. The experimental results demonstrate that the proposed algorithm can effectively detect the multi-source PD features in PRPD patterns under complex noise and thus successfully identify the types of multi-source PD. The mean Average Precision (mAP) can reach 98.4%. The precision of single-source PD and multi-source PD can reach 95.2% and 89.3%, when testing with field data.

Broadband 3-D Omnidirectional Magnetic Induction-Based Sensor Module for Partial Discharge Detection of High-Voltage Equipment in IIoT Application

Broadband 3-D Omnidirectional Magnetic Induction-Based Sensor Module for Partial Discharge Detection of High-Voltage Equipment in IIoT Application

A New Approach Based on Deep Features of Convolutional Neural Networks for Partial Discharge Detection in Power Systems

A New Approach Based on Deep Features of Convolutional Neural Networks for Partial Discharge Detection in Power Systems

Improving SNR and Sensing Distance of COTDR for Distributed Partial Discharge Detection in Power Cable Joints

Improving SNR and Sensing Distance of COTDR for Distributed Partial Discharge Detection in Power Cable Joints

Bioinspired Ultra High Frequency Antenna for Partial Discharge Detection in High-Voltage Equipment

Bioinspired Ultra High Frequency Antenna for Partial Discharge Detection in High-Voltage Equipment

Partial Discharge Detection Based on Ultrasound Using Optimized Deep Learning Approach

Partial Discharge Detection Based on Ultrasound Using Optimized Deep Learning Approach

A broadband mono‐cone antenna employing a shorted parasitic ring and parasitic metal pillars

AbstractThis letter proposes a broadband mono‐cone antenna with a shorted parasitic ring and parasitic metal pillars. Four parasitic metal pillars expand the high‐frequency operation band, while a shorted parasitic ring increases the current flow path and expands the low‐frequency operation band. A 148.3% impedance bandwidth for VSWR ≤ 2 from 0.831 to 5.6 GHz is attained. The antenna has stable radiation performance across the entire operation spectrum. The simulated and measured results show a good agreement. It is exceptionally compatible with GSM900, DCS1800, PCS1900, UMTS, IMT2000, WiBro, WiMAX2350/3500, LPWAN, wireless LAN (IEEE 802.11b), and a portion of the WLAN/Wi‐Fi frequency spectrum. Indoor applications such as wireless energy collection, partial discharge detection, and so on are possible for the proposed antenna.