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Keywords = offline PD measurements

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20 pages, 3602 KiB  
Article
Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine
by Alexandros Kolokas, Panagiotis Mallioris, Michalis Koutsiantzis, Christos Bialas, Dimitrios Bechtsis and Evangelos Diamantis
Machines 2024, 12(11), 762; https://doi.org/10.3390/machines12110762 - 30 Oct 2024
Cited by 2 | Viewed by 1131
Abstract
The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency [...] Read more.
The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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12 pages, 6504 KiB  
Project Report
Investigating the Effectiveness of an IMU Portable Gait Analysis Device: An Application for Parkinson’s Disease Management
by Nikos Tsotsolas, Eleni Koutsouraki, Aspasia Antonakaki, Stefanos Pizanias, Marios Kounelis, Dimitrios D. Piromalis, Dimitrios P. Kolovos, Christos Kokkotis, Themistoklis Tsatalas, George Bellis, Dimitrios Tsaopoulos, Paris Papaggelos, George Sidiropoulos and Giannis Giakas
BioMedInformatics 2024, 4(2), 1085-1096; https://doi.org/10.3390/biomedinformatics4020061 - 10 Apr 2024
Viewed by 1344
Abstract
As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth [...] Read more.
As part of two research projects, a small gait analysis device was developed for use inside and outside the home by patients themselves. The project PARMODE aims to record accurate gait measurements in patients with Parkinson’s disease (PD) and proceed with an in-depth analysis of the gait characteristics, while the project CPWATCHER aims to assess the quality of hand movement in cerebral palsy patients. The device was mainly developed to serve the first project with additional offline processing, including machine learning algorithms that could potentially be used for the second aim. A key feature of the device is its small size (36 mm × 46 mm × 16 mm, weight: 14 g), which was designed to meet specific requirements in terms of device consumption restrictions due to the small size of the battery and the need for autonomous operation for more than ten hours. This research work describes, on the one hand, the new device with an emphasis on its functions, and on the other hand, its connection with a web platform for reading and processing data from the devices placed on patients’ feet to record the gait characteristics of patients on a continuous basis. Full article
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17 pages, 3316 KiB  
Article
Metrological Qualification of PD Analysers for Insulation Diagnosis of HVDC and HVAC Grids
by Fernando Garnacho, Fernando Álvarez, Alf-Peter Elg, Christian Mier, Kari Lahti, Abderrahim Khamlichi, Eduardo Arcones, Joni Klüss, Armando Rodrigo Mor, Pertti Pakonen, José Ramón Vidal, Álvaro Camuñas, Jorge Rovira, Carlos Vera and Miran Haider
Sensors 2023, 23(14), 6317; https://doi.org/10.3390/s23146317 - 11 Jul 2023
Cited by 3 | Viewed by 1563
Abstract
On-site partial discharge (PD) measurements have turned out to be a very efficient technique for determining the insulation condition in high-voltage electrical grids (AIS, cable systems, GIS, HVDC converters, etc.); however, there is not any standardised procedure for determining the performances of PD [...] Read more.
On-site partial discharge (PD) measurements have turned out to be a very efficient technique for determining the insulation condition in high-voltage electrical grids (AIS, cable systems, GIS, HVDC converters, etc.); however, there is not any standardised procedure for determining the performances of PD measuring systems. In on-line and on-site PD measurements, high-frequency current transformers (HFCTs) are commonly used as sensors as they allow for monitoring over long distances in high-voltage installations. To ensure the required performances, a metrological qualification of the PD analysers by applying an evaluation procedure is necessary. A novel evaluation procedure was established to specify the quantities to be measured (electrical charge and PD repetition rate) and to describe the evaluation tests considering the measured influence parameters: noise, charge amplitude, pulse width and time interval between consecutive pulses. This procedure was applied to different types of PD analysers used for off-line measurements, sporadic on-line measurements and continuous PD monitoring. The procedure was validated in a round-robin test involving two metrological institutes (RISE from Sweden and FFII from Spain) and three universities (TUDelft from the Netherlands, TAU from Finland and UPM from Spain). With this round-robin test, the effectiveness of the proposed qualification procedure for discriminating between efficient and inappropriate PD analysers was demonstrated. Furthermore, it was shown that the PD charge quantity can be properly determined for on-line measurements and continuous monitoring by integrating the pulse signals acquired with HFCT sensors. In this case, these sensors must have a flat frequency spectrum in the range between several tens of kHz and at least two tens of MHz, where the frequency pulse content is more significant. The proposed qualification procedure can be useful for improving the future versions of the technical specification TS IEC 62478 and the standard IEC 60270. Full article
(This article belongs to the Special Issue Power and Electronic Measurement Systems)
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19 pages, 4602 KiB  
Article
New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids
by Abderrahim Khamlichi, Fernando Garnacho and Pascual Simón
Sensors 2023, 23(13), 5955; https://doi.org/10.3390/s23135955 - 27 Jun 2023
Cited by 7 | Viewed by 2014
Abstract
A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be [...] Read more.
A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be qualified by means of the metrological and diagnosis tests arranged in this calibrator. This synthetic PD calibrator can reproduce PD pulse trains of the same sequence as actual representative defects (cavity, surface, floating potential, corona, SF6 protrusion, SF6 jumping particles, bubbles in oil, etc.) acquired in HV equipment in service or by means of measurements made in HV laboratory test cells. The diagnostic capabilities and PD measurement errors of the PD analyzers using HFCT sensors can be determined. A new time parameter, “PD Time”, associated with any arbitrary PD current pulse i(t) is introduced for calibration purposes. It is defined as the equivalent width of a rectangular PD pulse with the same charge value and amplitude as the actual PD current pulse. The synthetic PD calibrator consists of a pulse generator that operates on a current loop matched to 50 Ω impedance to avoid unwanted reflections. The injected current is measured by a reference measurement system built into the PD calibrator that uses two HFCT sensors to ensure that the current signal is the same at the input and output of the calibration cage where the HFCT of the PD analyzer is being calibrated. Signal reconstruction of the HFCT output signal to achieve the input signal is achieved by applying state variable theory using the transfer impedance of the HFCT sensor in the frequency domain. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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17 pages, 9379 KiB  
Article
A Study on the Effectiveness of Partial Discharge Models for Various Electrical Machines’ Insulation Materials
by Dimosthenis Verginadis, Tryfon Iakovidis, Athanasios Karlis, Michael Danikas and Jose-Alfonso Antonino-Daviu
Machines 2023, 11(2), 230; https://doi.org/10.3390/machines11020230 - 4 Feb 2023
Cited by 3 | Viewed by 2261
Abstract
A vital component of electrical machines (EMs), which plays the most significant role in their reliable and proper operation, is their insulation system. Synchronous generators (SGs) are the most commonly used EMs in energy production and industry. Epoxy resin and mica are the [...] Read more.
A vital component of electrical machines (EMs), which plays the most significant role in their reliable and proper operation, is their insulation system. Synchronous generators (SGs) are the most commonly used EMs in energy production and industry. Epoxy resin and mica are the predominant insulation materials for the SGs’ windings because their characteristics and properties are suitable for extending the lifetime of the insulation. Partial discharges (PDs) are both a symptom of insulation degradation, as they cause serious problems for insulation, and a means to identify possible insulation faults with offline and/or online PD tests and measurements. A comparison of three different equivalent circuit models of PDs occurring in different insulation materials (epoxy resin, mica, and a combination of these two) is presented in this paper. Different applied voltages and/or various geometries of voids are the factors investigated through simulations. The number of PDs, PD activity, and flashover voltages are examined in order to evaluate which of the aforementioned materials has the best reaction against PD activity. Full article
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27 pages, 11542 KiB  
Article
Defects Classification of Hydro Generators in Indonesia by Phase-Resolved Partial Discharge
by Chun-Yao Lee, Nando Purba and Guang-Lin Zhuo
Mathematics 2022, 10(19), 3659; https://doi.org/10.3390/math10193659 - 6 Oct 2022
Cited by 2 | Viewed by 3305
Abstract
This paper proposed a phase-resolved partial discharge (PRPD) shape method to classify types of defect generator units by using offline partial discharge (PD) measurement instruments. In this paper, the experimental measurement was applied to two generators in the Inalum hydropower plant, located in [...] Read more.
This paper proposed a phase-resolved partial discharge (PRPD) shape method to classify types of defect generator units by using offline partial discharge (PD) measurement instruments. In this paper, the experimental measurement was applied to two generators in the Inalum hydropower plant, located in North Sumatera, Indonesia. The recorded PRPD using the instrument MPD600 can illustrate the PRPD patterns of generator defects. The proposed PRPD shape method is used to mark auxiliary lines on the PRPD patterns. Moreover, four types of defects refer to the IEC 60034-27 standard, which are microvoid (S1), delamination tape layer (S2), slot defect (S3), and internal delamination (S4) and are used to classify the defect types of the generators. The results show that the proposed method performs well to classify types of defect generator units. Full article
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14 pages, 4363 KiB  
Article
Time Evolution of Partial Discharges in a Dielectric Subjected to the DC Periodic Voltage
by Antonino Imburgia, Giuseppe Rizzo, Pietro Romano, Guido Ala and Roberto Candela
Energies 2022, 15(6), 2052; https://doi.org/10.3390/en15062052 - 11 Mar 2022
Cited by 4 | Viewed by 2515
Abstract
Partial discharge (PD) detection can be considered one of the most useful tools for assessing the insulation conditions of the power apparatus in high-voltage systems. Under AC conditions, this analysis is widely employed in online and offline tests, such as type tests or [...] Read more.
Partial discharge (PD) detection can be considered one of the most useful tools for assessing the insulation conditions of the power apparatus in high-voltage systems. Under AC conditions, this analysis is widely employed in online and offline tests, such as type tests or commissioning, and can be carried out by applying the phase-resolved PD (PRPD) method, since the patterns can give information about the defect classification. Under DC voltages, the classic pattern recognition method cannot be performed, and the measurements show complexities related to the nature of the phenomena. For this reason, to date, a standard for PD measurements under DC does not exist. In previous papers, a new method for PD detection under DC stress voltages has been proposed by the authors. It is based on the application of a direct current periodic (DCP) waveform useful in obtaining PRPD patterns. The dependence of partial discharge inception voltage (PDIV) and PD repetition rate (PDRR) on the δ shape parameter of the DCP for different materials, as well as the capability to recognize different discharge phenomena, provided valid indications on the behavior of PD in the transition from AC to DC. The aim of this paper is to evaluate the time dependence of PD occurring in a dielectric by applying the DCP waveform. In our previous studies, the investigations were focused on the PD behavior under different values of the DC voltage periodic part. In another work, the DCP waveform with both positive and negative polarity was applied to several dielectric materials. In the proposed work, instead, the DCP waveform is applied for a long time in order to observe its effect on the PD behavior for 72 h. In this way, due to the space charge accumulation phenomenon, the aging effect, also due to the space charge accumulation phenomenon, is evaluated. The methodological approach was to acquire PRPD patterns over time and evaluate their trends in comparison with the sinusoidal case. The experimental results show that, with a DCP waveform having δ = 0.6, the aging effect similar to that provided by pure DC stress is observed, while the acquired PRPD patterns are easily interpretated, as in the AC case. Full article
(This article belongs to the Section F6: High Voltage)
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21 pages, 2786 KiB  
Review
Review on Detection and Analysis of Partial Discharge along Power Cables
by Xiaohua Zhang, Bo Pang, Yaxin Liu, Shaoyu Liu, Peng Xu, Yan Li, Yifan Liu, Leijie Qi and Qing Xie
Energies 2021, 14(22), 7692; https://doi.org/10.3390/en14227692 - 17 Nov 2021
Cited by 41 | Viewed by 7830
Abstract
Partial discharge (PD) detection and analysis plays a crucial role for acceptance testing and condition monitoring of power cables. Various aspects are related to PD in power cables from theory to practice. This paper first summarizes the PD mechanism and models used for [...] Read more.
Partial discharge (PD) detection and analysis plays a crucial role for acceptance testing and condition monitoring of power cables. Various aspects are related to PD in power cables from theory to practice. This paper first summarizes the PD mechanism and models used for PD analysis in power cables. Afterwards, PD detection is addressed in the aspects of off-line test, on-line test, and sensors. PD analysis is discussed in detail. Specifically, related quantities and algorithms for PD analysis are outlined. PD characteristics with affecting factors, e.g., dielectric type, load, and applied voltage are discussed. Experience on PD development trend with measurements in field is analyzed. Based on the comprehensive review, challenges of PD detection and analysis along a power cable are proposed. Full article
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16 pages, 3451 KiB  
Review
Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress
by Sonia Barrios, David Buldain, María Paz Comech, Ian Gilbert and Iñaki Orue
Energies 2019, 12(13), 2485; https://doi.org/10.3390/en12132485 - 27 Jun 2019
Cited by 91 | Viewed by 8893
Abstract
This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for [...] Read more.
This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure. Full article
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18 pages, 12613 KiB  
Article
A Novel Partial Discharge Localization Method in Substation Based on a Wireless UHF Sensor Array
by Zhen Li, Lingen Luo, Nan Zhou, Gehao Sheng and Xiuchen Jiang
Sensors 2017, 17(8), 1909; https://doi.org/10.3390/s17081909 - 18 Aug 2017
Cited by 21 | Viewed by 6575
Abstract
Effective Partial Discharge (PD) localization can detect the insulation problems of the power equipment in a substation and improve the reliability of power systems. Typical Ultra-High Frequency (UHF) PD localization methods are mainly based on time difference information, which need a high sampling [...] Read more.
Effective Partial Discharge (PD) localization can detect the insulation problems of the power equipment in a substation and improve the reliability of power systems. Typical Ultra-High Frequency (UHF) PD localization methods are mainly based on time difference information, which need a high sampling rate system. This paper proposes a novel PD localization method based on a received signal strength indicator (RSSI) fingerprint to quickly locate the power equipment with potential insulation defects. The proposed method consists of two stages. In the offline stage, the RSSI fingerprint data of the detection area is measured by a wireless UHF sensor array and processed by a clustering algorithm to reduce the PD interference and abnormal RSSI values. In the online stage, when PD happens, the RSSI fingerprint of PD is measured via the input of pattern recognition for PD localization. To achieve an accurate localization, the pattern recognition process is divided into two steps: a preliminary localization is implemented by cluster recognition to reduce the localization region, and the compressed sensing algorithm is used for accurate PD localization. A field test in a substation indicates that the mean localization error of the proposed method is 1.25 m, and 89.6% localization errors are less than 3 m. Full article
(This article belongs to the Section Physical Sensors)
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