Discharge Detection Research Articles

Towards the automated detection of interictal epileptiform discharges with magnetoencephalography

BackgroundThe analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on subjective criteria. New MethodHere, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. We tested our pipelines on resting-state MEG recordings from 10 school-aged children with (multi)focal epilepsy. ResultsIn focal epilepsy patients, both pipelines successfully detected visually identified IEDs, but also revealed unidentified low-amplitude IEDs. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci—an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. Comparison with existing methodsWe compared our results with visual IED detection by an experienced clinical magnetoencephalographer, getting heightened sensitivity and requiring minimal input from clinical practitioners. ConclusionsIED detection based on ICA or HMM represents an efficient way to identify IED localization and timing. The development of these automatic IED detection algorithms provide a step forward in clinical MEG practice by decreasing the duration of MEG analysis and enhancing its sensitivity.

Synthesis and Characterization of High‐Quality Ta2O5 Nanoparticles for Highly Selective SAW‐Based DUV Sensor and Corona Detection Application

AbstractIn recent times, significant progress is made in the development of solar‐blind deep‐ultraviolet (DUV) photodetectors (200–280 nm) due to their potential applications for military and civil purposes. In this study, the development of a novel solar‐blind DUV photodetector based on tantalum pentoxide (Ta2O5) nanoparticles (NPs) utilizing Surface Acoustic Wave (SAW) sensor integrated with Interface electronics is reported for the first time. The 3‐D Ta2O5 NPs surface morphology, crystallinity, and defect state level is discussed using various analytical techniques, including scanning electron microscope (SEM), X‐ray diffraction (XRD), and spectrophotometer. The developed 3‐D Ta2O5 NPs based photodetector exhibits high sensitivity to 254 nm DUV irradiation (5 µW cm−2), demonstrating excellent photoresponse characteristics. The performance parameters including highest sensitivity, fast response/recovery time are measured as 1621.1 ppm (mW cm−2)−1, 3.7s/19.8s, respectively. Subsequent analysis demonstrates the stability and repeatability of the photodetector, enabling its potential to real time corona discharge detection. With its inherent advantages, the current DUV photodetector utilizing Ta2O5 NPs exhibits great potential for future applications in DUV optoelectronics.

Detection of Arc Discharge through Sound Event Detection Techniques and Public Datasets

Arc discharge in switchboard is a potential fire hazard and caused by short-circuits in power supply cords with damaged electrical insulation. Arc detection based on electric current and resistance can be destructed while arc discharge occurs due to circuit contact, and humidity condition of switchboard affects the detection performance of optical signal-based arc detection. This paper proposes a deep learning-based arc discharge detection that uses sound event detection (SED) method. The neural network was trained on public datasets to exclude the processfor obtainingsound dataset of arc discharge. Filter-augmentation techniques were applied to account for the acoustical differences between the public dataset and target environments. The network uses cepstrum in addition to STFT as network input feature for effective extraction of harmonic spectral patterns of arc discharge. The results show that the proposed neural network achieved an F1 score of 0.83 and accuracy of 0.89 when tested on a test-bed with arc discharge generator based on UL1699.

Tri-axial rubidium and helium optically pumped magnetometers for on-scalp magnetoencephalography recording of interictal epileptiform discharges: a case study

Cryogenic magnetoencephalography (MEG) enhances the presurgical assessment of refractory focal epilepsy (RFE). Optically pumped magnetometers (OPMs) are cryogen-free sensors that enable on-scalp MEG recordings. Here, we investigate the application of tri-axial OPMs [87Rb (Rb-OPM) and 4He gas (He-OPM)] for the detection of interictal epileptiform discharges (IEDs). IEDs were recorded simultaneously with 4 tri-axial Rb- and 4 tri-axial He-OPMs in a child with RFE. IEDs were identified visually, isolated from magnetic background noise using independent component analysis (ICA) and were studied following their optimal magnetic field orientation thanks to virtual sensors. Most IEDs (>1,000) were detectable by both He- and Rb-OPM recordings. IEDs were isolated by ICA and the resulting magnetic field oriented mostly tangential to the scalp in Rb-OPMs and radial in He-OPMs. Likely due to differences in sensor locations, the IED amplitude was higher with Rb-OPMs. This case study shows comparable ability of Rb-OPMs and He-OPMs to detect IEDs and the substantial benefits of triaxial OPMs to detect IEDs from different sensor locations. Tri-axial OPMs allow to maximize spatial brain sampling for IEDs detection with a limited number of sensors.

The Practical Application of Bio-Inspired PMA for the Detection of Partial Discharges in High Voltage Equipment

In this paper, the practical application of a bio-inspired antenna for partial discharge (PD) detection in high voltage equipment was evaluated in order to validate the efficiency of using this technology for PD monitoring purposes. For this, PD measurements using the bio-inspired antenna were performed on operational 69 kV potential transformers (PT) in a real substation. After the field experiment, laboratory measurements using the IEC 60270 standard method and a bio-inspired antenna were performed, simultaneously, over the evaluated PT. The results obtained at the substation indicated suspicious frequencies of partial discharge activity in two out of three evaluated potential transformers, mainly for the frequencies of 461 MHz, 1366 MHz, 1550 MHz and 1960 MHz. During the laboratory tests, the presence of partial discharge activity over the suspicious potential transformers was confirmed with the detection of PD apparent charge levels above 20 pC. Finally, the frequency spectrum obtained from the PD signals detected by the bio-inspired antenna in the laboratory presented similar frequency values to those obtained during the practical application at the substation, making it a promising indicator for future defect classification studies using artificial intelligence.

A partial discharge detection system based on improved STMS fiber UV sensor

The occurrence of partial discharge in high-voltage transmission equipment can lead to insulation breakdown, emphasizing the critical need for early fault detection. Traditional sensors are highly vulnerable to electromagnetic interference, which significantly impacts the accuracy and reliability of the obtained results. This paper presents an all-fiber ultraviolet (UV) sensor based on the STMS structure coated with zinc oxide (ZnO) composite graphene, realizing partial discharge detection based on optical theory. Firstly, using first principles calculation method, we conducted a comparative study on the ultraviolet-sensitive materials ZnO and ZnO composite graphene. The results demonstrate that the composite material exhibits optical response characteristics that are more suitable for partial discharge detection. Subsequently, sensitive materials with different surface morphology were prepared by hydrothermal method. These materials were uniformly coated on the waist cone region of the STMS optical fiber sensing unit using surface functionalization techniques. The UV test results showed that the flower-shaped ZnO composite graphene coated STMS optical fiber sensing has a higher sensitivity of 857.41 pm/nW cm−2, reaching 1.62 times that of rod-shaped sensitive materials. Finally, the test results of the partial discharge environment indicated that the sensor showed a good linear response when the partial discharge voltage was above 21.55 kV. The UV optical fiber sensor demonstrates promising potential for applications in the field of partial discharge detection.

Detection of Interictal epileptiform discharges with semi-supervised deep learning

Interictal discharges (IEDs) in EEG recordings are important signatures of epilepsy as their presence is strongly associated with an increased risk of seizures. IEDs are relatively short-duration events (typically 70–250 ms) that can be viewed as stochastic anomalies in such recordings. Currently, visual analysis of the EEG by clinical experts is the gold standard. This process, however, is time-consuming, error prone, and associated with a long learning period.Automatizing the detection of IEDs has the potential to significantly reduce review time, and may serve to complement the visual analysis. Supervised deep learning methods have shown potential for this purpose, but the scarceness of annotated data has limited their performance, which motivates to explore unsupervised and semi-supervised approaches, that do not require (extensive) expert annotations.We trained different unsupervised deep learning models, Autoencoders (AE) and Variational Autoencoders (VAE) for anomaly (IED) detection in these recordings. These models are dimensionality reduction based approaches, that can compress the data to lower dimensional representations, learning the notion of normality within data and reconstruct samples accordingly. Our data set comprised 203 clinical EEGs, 115 from patients with epilepsy, that contained IEDs, and 88 normal EEGs. Performance was assessed qualitatively through visual analysis of reconstructed samples and quantified as Area Under the Curve (AUC), sensitivity and specificity.The best performance was obtained using a semi-supervised approach, allowing the detection of IEDs with a sensitivity of 81.9% and specificity of 91.7%.Our work shows that unsupervised approaches and other approaches with limited supervision perform satisfactorily and have the potential to assist visual assessment of interictal discharges in epilepsy diagnostics.

A Novel Approach to Partial Discharge Detection Under Repetitive Unipolar Impulsive Voltage

Partial discharge (PD) detection is an important method to verify the reliability of the insulation structure of high voltage power electronics devices. Although there are many researches on PD detection inside power modules, they mainly focus on traditional electrical detection methods and 50 Hz working conditions, which do not meet the repetitive unipolar impulsive voltage conditions of power modules, and traditional methods cannot solve the electromagnetic interference (EMI) problem under working conditions. This paper proposes a novel approach to PD detection in power modules. This approach designs an optic fiber sensing (OFS) system based on optic theory, and conducts the detailed mathematical theoretical deduction. In addition, A partial discharge inception voltage (PDIV) signal processing method based on PD acoustic signal is proposed. Finally, the proposed approach is applied to a power module. The PD signal and PDIV is successfully captured without EMI interference and new insulation problems.

Predicting Severe COVID-19 Infection on Initial Diagnosis: Comparison and Validation of CT Imaging Triage Tools.

Developing a reliable predictive tool of disease severity in COVID-19 infection is important to help triage patients and ensure the appropriate utilization of health-care resources. To develop, validate, and compare three CT scoring systems (CTSS) to predict severe disease on initial diagnosis of COVID-19 infection. One hundred and twenty and 80 symptomatic adults with confirmed COVID-19 infection who presented to emergency department were evaluated retrospectively in the primary and validation groups, respectively. All patients had non-contrast CT chest within 48 hours of admission. Three lobarbased CTSS were assessed and compared. The simple lobar system was based on the extent of pulmonary infiltration. Attenuation corrected lobar system (ACL) assigned further weighting factor based on attenuation of pulmonary infiltrates. Attenuation and volume-corrected lobar system incorporated further weighting factor based on proportional lobar volume. The total CT severity score (TSS) was calculated by adding individual lobar scores. The disease severity assessment was based on Chinese National Health Commission guidelines. Disease severity discrimination was assessed by the area under the receiver operating characteristic curve (AUC). The ACL CTSS demonstrated the best predictive and consistent accuracy of disease severity with an AUC of 0.93(95%CI:0.88-0.97) in the primary cohort and 0.97 (95%CI:0.91.5-1) in the validation group. Applying a TSS cut-off value of 9.25, the sensitivities were 96.4% and 100% and the specificities were 75% and 91% in the primary and validation groups, respectively. The ACL CTSS showed the highest accuracy and consistency in predicting severe disease on initial diagnosis of COVID-19. This scoring system may provide frontline physicians with a triage tool to guide admission, discharge, and early detection of severe illness.

SNR enhancement of quasi-distributed weak acoustic signal detection by elastomers and MMF integrated Φ-OTDR.

We have proposed and demonstrated a weak acoustic signal detection technology based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Non-contact acoustic signals transmitting through air gap between the sound source and the receiver are difficult to detect due to fast attenuation. In order to improve the detection ability of non-contact weak acoustic signals, we demonstrate that multi-mode fiber (MMF) is a better solution than single-mode fiber (SMF) benefiting from its larger core and higher Rayleigh backscattering (RBS) capture coefficient. The frequency signal-to-noise ratio (SNR) has been enhanced by 9.26 dB. Then, with the help of 3D printing technology, elastomers have been designed to further enhance the detection ability due to the high-sensitive response to acoustic signals. Compared with the previous reported "I" type elastomer, the location and frequency SNR enhancement caused by our new proposed "n" type elastomer are 8.39 dB and 11.02 dB in SMF based system. The values are further improved to 10.51 dB and 13.38 dB in MMF and "n" type elastomer integrated system. And a phase-pressure sensitivity of -94.62 dB re rad/µPa has been achieved at 2.5 kHz. This non-contact weak acoustic signal detection technique has great application potential in the quasi-distributed partial discharge (PD) detection of smart grid.

Use of Plat-Bar Electrode Media to Detect Partial Discharge in Epoxy Resin Materials on PCB Surfaces

The electric power system must have a protection system with reliable electric power isolation, so that the supply of electrical energy works properly and optimally. There are several kinds of disturbances in the electric power distribution system, one of which is a partial discharge. This study aims to investigate the effect of plate-rod electrodes on partial discharge, partial discharge voltage limits in solid insulation, and the characteristics of partial discharge in electrode media, and waveforms in partial discharge detection sensors. After conducting research, it was found that in the plate-rod type electrode media, the PDIV voltage appeared earlier than the plate-plate electrode media due to the cross-sectional area of the electrode media. And the limit of the pre-discharge voltage of PCB type solid insulation on the plate-bar electrodes is at a voltage of 1.3 kV. Several indicators to determine the characteristics of the partial discharge are background noise off (BGN off), background noise on (BGN on), partial discharge inception voltage (PDIV), and partial discharge voltage. After being examined, the value of each partial discharge detection voltage was obtained at negative and positive polarity voltages on the electrode plates and plate needles respectively 2.85 kV, 2.95 kV and 1.32 kV, 1.57 kV.

Hilbert Fractal Antenna for Abnormal Discharge Detection of Transmission Equipment

The safe and stable operation of the power system can ensure people’s basic electricity needs. It also plays an important role in energy security and economic development and can contribute to environmental protection and energy conservation. If there are problems in the power system, it will not only affect people’s normal life and work but also lead to power loss or waste, thereby exacerbating the impact on the environment. With the long-term operation of transmission equipment, insulation devices are aging, leading to frequent abnormal discharges and affecting the safety and stability of the power system. Therefore, timely detection and troubleshooting are necessary. When abnormal discharge occurs in transmission equipment, it will radiate radio frequency (RF) electromagnetic waves, which are mainly concentrated between 200 MHz and 300 MHz. RF antennas that can capture signals in this frequency band can be used for detection to determine whether abnormal discharge has occurred. This article uses the Hilbert fractal antenna as the research object to analyze the impact of different orders on antenna performance and optimizes the different feeding point positions of the antenna. Finally, the antenna structure for detecting abnormal discharge is obtained. By making an actual antenna and conducting experimental analysis, the discharge detection function of the designed antenna is verified.

Analysis of Partial Discharge Fault Diagnosis Technology for High Voltage Switch Cabinet

In order to enhance the security and reliability of high-voltage switch cabinet operation, the diagnosis of internal discharge defects in high-voltage switch cabinet equipment is analyzed in this paper. Firstly, a variety of partial discharge live detection technologies such as three-dimensional voiceprint monitoring technology and three-dimensional ultraviolet technology for partial discharge are studied. Then, a variety of partial discharge live detection technologies are constructed. Finally, the data fusion of the relevant detection data is carried out, and the monitoring results of the equipment’s running state are analyzed. The analysis results show that the data sensitivity and specificity of creepage, non-breakdown discharge, and breakdown discharge at different output values in the application of various partial discharge detection technologies have obvious effects. It is believed that the technology constructed in this study can improve detection accuracy and equipment safety. It provides important parameters and a basis for the maintenance of high-voltage switch cabinets. It is of extremely good importance to take partial live detection measures for the operating switch cabinet and provides a reference for field application.

Development of gas chromatography for dissolved gas in oil based on nitrogen plasma discharge detection principle

The gas chromatography detection technology of dissolved gas in oil based on the nitrogen plasma discharge detection principle was studied. This technology detected the characteristic emission light emitted by the excited molecule after the ionization of the detected molecule motivated by the plasma generated by the nitrogen molecule under the action of a high-energy electromagnetic field and realized the quantitative detection of different molecules. Compared with the previous national standard commonly used hydrogen flame ionization detector (FID) and thermal conductivity detector (TCD) gas chromatography, it has a lower detection limit. The advanced chromatographic system with the original process realizes the detection of dissolved gas in transformer oil, which has good linearity and repeatability and can ensure the accuracy of measurement data. The analysis only used nitrogen and did not use hydrogen to prevent explosion hazards. Nitrogen is at least ten times cheaper than helium and is more economical than helium ion chromatography in consumables. The device can detect the small content of the characteristic gas in the transformer oil and the slight change of the content to prevent the occurrence of dangerous accidents.

High-sensitivity fiber optic Fabry-Perot ultrasonic sensor based on a grooved silicon diaphragm for partial discharge detection.

An extrinsic fiber optic Fabry-Perot interferometric (EFPI) ultrasonic sensor based on a grooved silicon diaphragm for partial discharge (PD) detection has been proposed. The size of the groove is determined by finite element simulation, which allows the resonant frequency of the sensor to meet the requirements of PD ultrasonic detection and improves the sensitivity of the sensor by 5.07 times compared with that based on a traditional circular diaphragm. The microelectro-mechanical system process is used to fabricate the diaphragm on a silicon-on-insulator wafer, and the prepared diaphragm has a grooved section with a diameter of 829.34µm and a thickness of only 2.09µm. At its resonant frequency of 61.5kHz, the acoustic pressure sensitivity of the sensor is 172.42mV/Pa. The ultrasonic signal detection capability of the sensor is verified in the PD experiment. Furthermore, the characteristics of the corona discharge are successfully manifested based on the ultrasonic waves detected by the EFPI sensor. It is demonstrated that the proposed sensor is suitable for PD detection due to its high sensitivity, simple production process, and good resistance to environmental interference.

3D printed Fabry-Perot acoustic probe with a glass horn tube

Sensing an incoming acoustic signal is typically associated with absorbing the energy, perturbing the measurement and therefore causing a deformation of the sensing elements, which is mainly related to the acoustic wave pressure. Here, we demonstrated a fiber-tip based Fabry-Perot (FP) acoustic probe sensor, which was directly printed on an optical fiber tip by a two-photon 3D printing technique and assembled by a glass horn structure, which can improve the sensitivity. It showed that the sensor has a −3 dB bandwidth of 366.05 kHz at the first resonant frequency of 467.84 kHz. A low noise-limited minimum detectable pressure level of 4.71 mPa/Hz1/2@100 kHz is obtained. Due to the acoustic wave focusing property of the horn structure, the detected signal intensity can be amplified by 4 times as the sensor located at the bottom position. It demonstrates that 3D printed micro acoustic devices could be used for weak acoustic wave detection in the applications of partial discharge, photoacoustic imaging and non-destructive detection.

A Data Set of Signals from an Antenna for Detection of Partial Discharges in Overhead Insulated Power Line

We introduce a data set obtained via a contactless antenna method for detecting partial discharges in XLPE-covered conductors used in medium-voltage overhead power transmission lines. The data set consists of almost three years’ worth of data, collected every hour from 9 measuring stations in Czechia and Slovakia. Each sample in the data set represents a single signal gathered for 20 ms. The contactless method is deployed on the same stations as the galvanic contact method, which is used by power distributors and can provide ground truth. Also manually curated data by human expert are present. Successful detection of partial discharges can prevent electricity shutdowns and forest fires resulting from insulation failure due to vegetation contact. The data set is particularly relevant for covered conductors used in mountainous regions where establishing a safe zone is challenging. The contactless method offers advantages such as cheaper and easier installation. The data set has the potential to develop machine learning models to detect partial discharges and facilitate safer and cheaper use of covered conductors.

Gas Production Characteristics of UHV Oil immersed Shunt Reactor with Vibration Defects

The special multi-air gap structure of Ultra high voltage (UHV) oil-immersed shunt reactor determines that its magnetic leakage is far greater than that of a power transformer of the same voltage level, and it is more prone to vibration and other problems. Overheating and suspended discharge caused by vibration are important sources of excessive gas production in UHV shunt reactors. In this paper, the source of gas production under reactor vibration is analyzed, and a reactor defect simulation gas production platform is built. Using oil chromatography analysis, partial discharge detection, and other means, the gas production characteristics, discharge characteristics and insulation degradation characteristics of typical defects such as suspended discharge and partial overheating under vibration have been experimentally studied. Then, the correlation analysis model between the vibration type, intensity, overheating degree, and the reactor performance change was established. Finally, the gas production and discharge characteristics of the reactor under different defect types were obtained. Besides, gas production characteristics under vibration with 50 Hz, 100 Hz, and 150 Hz were carried out, and it was found that the gas will continue to increase with the increase of vibration frequency, and the growth has been stabilized after exceeding 100 Hz. The relevant research results of this paper can provide technical support for the application of UHV oil-immersed shunt reactor state assessment and diagnosis, and improve the level of equipment operation and maintenance.

Comprehensive Simulation Research on Microprocess and Macroscopic Electromagnetic Signals of Surface Discharge in SF6

The surface discharge on the dielectric is a common type of partial discharge in GIS, but the microscopic development process of surface discharge in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> is unclear. To find out the micro-process of surface discharge and its correlation mechanism with the partial discharge detection signals of GIS, the fluid-chemical simulation model of the streamer is used to simulate the micro-process of surface discharge in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> . Then the current signal obtained by the micro-process simulation is used as the excitation to simulate the electromagnetic signals based on the FITD method. A close connection between the microscopic discharge and the macroscopic signals is established. The results show that the electron concentration at the streamer head and the streamer velocity decrease first and then increase during the discharge, with an average velocity of about 9.84×105m/s. The discharge current reached a peak value of about 0.22A when the surface streamer reaches the cathode. In terms of waveform correlation, the absolute value of the average correlation coefficient between the electric field signals simulated in this paper and the actual measured signals is above 0.7, which is much higher than the simulated signal excited by the Gaussian source. By changing the applied voltage and comparing with the experiment results, it is found that the simulation method in this paper can reflect the change of signals under the influence of external conditions. As the applied voltage rises, the amplitude of the electric field signals increases, and the main frequency component on the frequency spectrum grows, with the main frequency value at about 1.2 GHz. The research in this paper is an effective supplement to the existing study on the microscopic mechanism of partial discharge signals.

A Contemporary Review of High Voltage Partial Discharge Detection and Recognition Techniques

This review article provides a summary of the most advanced approaches and advancements in the detection and recognition of high voltage partial discharge (PD). It discusses numerous detecting technologies, such as electrical, acoustic, and optical approaches, as well as their merits and disadvantages. It also discusses current developments in signal processing and pattern recognition algorithms used for PD detection and classification. Lastly, the study covers the challenges and limitations in high voltage PD detection and identification studies, as well as potential future solutions.