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Khamlichi A, Garnacho F, Simón P. New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids. SENSORS (BASEL, SWITZERLAND) 2023; 23:5955. [PMID: 37447804 PMCID: PMC10346474 DOI: 10.3390/s23135955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
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 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.
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Affiliation(s)
- Abderrahim Khamlichi
- Fundación para el Fomento de la Innovación Industrial, FFII-LCOE, Eric Kandel Street 1, 28906 Madrid, Spain; (F.G.); (P.S.)
- Departamento de Ingeniería Eléctrica, Electrónica, Automática y Física Aplicada, ETSIDI, Universidad Politécnica de Madrid, 28006 Madrid, Spain
| | - Fernando Garnacho
- Fundación para el Fomento de la Innovación Industrial, FFII-LCOE, Eric Kandel Street 1, 28906 Madrid, Spain; (F.G.); (P.S.)
- Departamento de Ingeniería Eléctrica, Electrónica, Automática y Física Aplicada, ETSIDI, Universidad Politécnica de Madrid, 28006 Madrid, Spain
| | - Pascual Simón
- Fundación para el Fomento de la Innovación Industrial, FFII-LCOE, Eric Kandel Street 1, 28906 Madrid, Spain; (F.G.); (P.S.)
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2
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Influence of the Cable Accessories Installing Method on the Partial Discharge Activity in Medium Voltage Cables. ENERGIES 2022. [DOI: 10.3390/en15124216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This article proposes a method to modify the construction of a medium voltage (MV) heat shrinkable cable termination in cases of atypical damage to the shields of cross-linked polyethylene (XLPE) insulated cables. The proposed solutions include a modified method of assembling electric field control coating. An attempt was made to check the effect of such damage to the shields of MV cables with XLPE insulation on the level of occurrence of partial discharges within the cable termination. The investigations included testing the XRUHAKXS 1 × 240/25 cable type using the electric method (ME) and high frequency (HF) method with sinusoidal AC test voltage. As a result of the measurements, the values of total charges in the period and phase-resolved partial discharge (PRPD) patterns were obtained. The presented experimental results show the influence of the damage of the semiconducting coating surface on the occurrence of a defect in the cable termination without a modified method of control mantissa pinning. We suggest new methods of assembling MV cable accessories in the case of the presented coating damage in MV cable insulation.
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Interpretable Detection of Partial Discharge in Power Lines with Deep Learning. SENSORS 2021; 21:s21062154. [PMID: 33808568 PMCID: PMC8003486 DOI: 10.3390/s21062154] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/12/2021] [Accepted: 03/17/2021] [Indexed: 11/20/2022]
Abstract
Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.
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Nguyen PD, Vo HQ, Le LN, Eo S, Kim L. An IoT Hardware Platform Architecture for Monitoring Power Grid Systems based on Heterogeneous Multi-Sensors. SENSORS 2020; 20:s20216082. [PMID: 33114629 PMCID: PMC7663348 DOI: 10.3390/s20216082] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 11/16/2022]
Abstract
Partial discharge (PD) is a major indicator of various failures in power grid systems. PD exhibits a physical occurrence where a localized electrical discharge happens in insulation materials. This phenomenon causes damage to the insulating parts and, in various circumstances, leads to complete insulation breakdown. As a consequence, it can produce more costly outcomes such as abrupt outages or lost production. Therefore, PD detection plays a vital role in preventing insulation failure. In this work, we propose a system that utilizes heterogeneous sensors for the PD detection along with multi-sensor interface, real-time advanced denoise processing, flexible system operation, and Bluetooth-low-energy (BLE)-based ad hoc communication. Among the variety of heterogeneous sensors, several are developed by the application of nanomaterials and nanotechnology, thus outperforming the regular types. The proposed system successfully identifies the presence of PD from sensor signals using a microprocessor-based processing system and effectively performs an advanced denoising technique based on the wavelet transform through field-programmable-gate-array (FPGA)-based programmable logics. With the development of the system, we aim to achieve a solution with low cost, high flexibility and efficiency, and ease of deployment for the monitoring of power grid systems.
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Abstract
Recently, a lot of research has been carried out on partial discharge (PD) using machine learning techniques. However, most of these studies have focused on the identification of multiple PD sources, PD classification, or denoising PD measurements, with few studies on real-time PD occurrence detection. In this paper, we propose a method to detect PD occurrence based on anomaly pattern detection. The proposed method consists of three steps. First, in the data preprocessing step, the pulse sequence data are converted into a feature vector stream by applying a sliding window technique. In the next step, normal data modeling is performed using feature vectors transformed from pulse sequence data collected in a normal state where no PD occurs. Finally, for the monitored pulse sequence, an online process for PD detection is carried out through conversion to a feature vector data stream and an anomaly pattern detection method. Experimental results using simulated PD data demonstrate the capabilities of the proposed method.
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6
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Fang J. Tightly integrated genomic and epigenomic data mining using tensor decomposition. Bioinformatics 2019; 35:112-118. [PMID: 29939222 DOI: 10.1093/bioinformatics/bty513] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 06/21/2018] [Indexed: 12/12/2022] Open
Abstract
Motivation Complex diseases such as cancers often involve multiple types of genomic and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data demands methods for integrating the multidimensional data in order to elucidate complex relationships among different types of genomic and epigenomic abnormalities. Results In the present study, we propose a tightly integrated approach based on tensor decomposition. Multiple types of data, including mRNA, methylation, copy number variations and somatic mutations, are merged into a high-order tensor which is used to develop predictive models for overall survival. The weight tensors of the models are constrained using CANDECOMP/PARAFAC (CP) tensor decomposition and learned using support tensor machine regression (STR) and ridge tensor regression (RTR). The results demonstrate that the tensor decomposition based approaches can achieve better performance than the models based individual data type and the concatenation approach. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jianwen Fang
- Computational & Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr., Rockville, MD, USA
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Imaging Time Series for the Classification of EMI Discharge Sources. SENSORS 2018; 18:s18093098. [PMID: 30223496 PMCID: PMC6163566 DOI: 10.3390/s18093098] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 09/11/2018] [Accepted: 09/12/2018] [Indexed: 11/24/2022]
Abstract
In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome.
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Mitiche I, Morison G, Nesbitt A, Hughes-Narborough M, Stewart BG, Boreham P. Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features. SENSORS (BASEL, SWITZERLAND) 2018; 18:E406. [PMID: 29385030 PMCID: PMC5856049 DOI: 10.3390/s18020406] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 01/22/2018] [Accepted: 01/26/2018] [Indexed: 11/16/2022]
Abstract
Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert's knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring.
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Affiliation(s)
- Imene Mitiche
- Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK.
| | - Gordon Morison
- Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK.
| | - Alan Nesbitt
- Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK.
| | | | - Brian G Stewart
- Institute of Energy and Environment, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK.
| | - Philip Boreham
- Innovation Centre for Online Systems, 7 Townsend Business Park, Bere Regis BH20 7LA, UK.
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Alizadeh M, Conklin CJ, Middleton DM, Shah P, Saksena S, Krisa L, Finsterbusch J, Faro SH, Mulcahey MJ, Mohamed FB. Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images. Magn Reson Imaging 2017; 47:7-15. [PMID: 29154897 DOI: 10.1016/j.mri.2017.11.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Revised: 11/11/2017] [Accepted: 11/13/2017] [Indexed: 01/18/2023]
Abstract
PURPOSE Ghost artifacts are a major contributor to degradation of spinal cord diffusion tensor images. A multi-stage post-processing pipeline was designed, implemented and validated to automatically remove ghost artifacts arising from reduced field of view diffusion tensor imaging (DTI) of the pediatric spinal cord. METHOD A total of 12 pediatric subjects including 7 healthy subjects (mean age=11.34years) with no evidence of spinal cord injury or pathology and 5 patients (mean age=10.96years) with cervical spinal cord injury were studied. Ghost/true cords, labeled as region of interests (ROIs), in non-diffusion weighted b0 images were segmented automatically using mathematical morphological processing. Initially, 21 texture features were extracted from each segmented ROI including 5 first-order features based on the histogram of the image (mean, variance, skewness, kurtosis and entropy) and 16s-order feature vector elements, incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence matrices in directions of 0°, 45°, 90° and 135°. Next, ten features with a high value of mutual information (MI) relative to the pre-defined target class and within the features were selected as final features which were input to a trained classifier (adaptive neuro-fuzzy interface system) to separate the true cord from the ghost cord. RESULTS The implemented pipeline was successfully able to separate the ghost artifacts from true cord structures. The results obtained from the classifier showed a sensitivity of 91%, specificity of 79%, and accuracy of 84% in separating the true cord from ghost artifacts. CONCLUSION The results show that the proposed method is promising for the automatic detection of ghost cords present in DTI images of the spinal cord. This step is crucial towards development of accurate, automatic DTI spinal cord post processing pipelines.
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Affiliation(s)
- Mahdi Alizadeh
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States.
| | - Chris J Conklin
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Devon M Middleton
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Pallav Shah
- Department of Radiology, Temple University, Philadelphia, PA, United States
| | - Sona Saksena
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Laura Krisa
- Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Scott H Faro
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | - M J Mulcahey
- Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B Mohamed
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
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Wang H, Li J, Wang D, Huang Z. A novel method of fuzzy fault tree analysis combined with VB program to identify and assess the risk of coal dust explosions. PLoS One 2017; 12:e0182453. [PMID: 28793348 PMCID: PMC5549981 DOI: 10.1371/journal.pone.0182453] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2017] [Accepted: 07/18/2017] [Indexed: 11/19/2022] Open
Abstract
Coal dust explosions (CDE) are one of the main threats to the occupational safety of coal miners. Aiming to identify and assess the risk of CDE, this paper proposes a novel method of fuzzy fault tree analysis combined with the Visual Basic (VB) program. In this methodology, various potential causes of the CDE are identified and a CDE fault tree is constructed. To overcome drawbacks from the lack of exact probability data for the basic events, fuzzy set theory is employed and the probability data of each basic event is treated as intuitionistic trapezoidal fuzzy numbers. In addition, a new approach for calculating the weighting of each expert is also introduced in this paper to reduce the error during the expert elicitation process. Specifically, an in-depth quantitative analysis of the fuzzy fault tree, such as the importance measure of the basic events and the cut sets, and the CDE occurrence probability is given to assess the explosion risk and acquire more details of the CDE. The VB program is applied to simplify the analysis process. A case study and analysis is provided to illustrate the effectiveness of this proposed method, and some suggestions are given to take preventive measures in advance and avoid CDE accidents.
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Affiliation(s)
- Hetang Wang
- Key Laboratory of Coal Methane and Fire Control (China University of Mining and Technology), Ministry of Education, Xuzhou, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, China
- School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
- * E-mail:
| | - Jia Li
- Key Laboratory of Coal Methane and Fire Control (China University of Mining and Technology), Ministry of Education, Xuzhou, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, China
| | - Deming Wang
- Key Laboratory of Coal Methane and Fire Control (China University of Mining and Technology), Ministry of Education, Xuzhou, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, China
| | - Zonghou Huang
- Key Laboratory of Coal Methane and Fire Control (China University of Mining and Technology), Ministry of Education, Xuzhou, China
- School of Safety Engineering, China University of Mining and Technology, Xuzhou, China
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