UHF Partial Discharge Localization in Gas-Insulated Switchgears: Gradient Boosting Based Approach

Darwish, Ahmad; Refaat, Shady S.; Abu‐Rub, Haitham; Toliyat, H.A.

doi:10.1109/kpec47870.2020.9167534

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…The monitoring system is comprised of three sections, namely data pre-processing, feature extraction, and development of the classification model. In the data pre-processing module, the data acquired from the sensors is cleaned from inconsistent measurements, missing data points, and outliers to sidestep any possible false interpretations in the subsequent steps [32,33]. Next, data encoding, correlation analysis, and data normalization are implemented to feed in the required data into the AI model used.…”

Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 99%

Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms

Saleh

Refaat

Khatri

et al. 2022

2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE)

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Due to high electric stresses in power equipment, insulation degradation has been prevalent as a result of increased PD exposure. In this paper, we study different machine learning (ML) methods for the detection and classification of partial discharges (PDs) for assessing the reliability of insulation systems. We introduce and examine a set of features using selected machine learning-based algorithms. The aim is to detect and classify PDs transpiring within insulation systems. Therefore, this paper presents tools to detect defects using suitable PD sensors and Machine Learning algorithms to facilitate diagnostics and enhance isolation system design. Experiments are being conducted on several voids in the insulator with varying shapes and sizes. A PD sensor is used for detecting the PDs taking place. Due to the presence of noise and other external interferences, appropriate filters and denoising methods are implemented. After that, the relevant PD features, such as the PD magnitude, PD repetition rate, statistical features, wavelet features, etc., are extracted. This study attempts to emphasize the importance of classifying the type of defect, as this will allow engineers to determine the severity of the fault taking place, and take the proper countermeasures.

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Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 99%

Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms

Saleh

Refaat

Khatri

et al. 2022

2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE)

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“…Several milestones have been reached in presenting Machine Learning (ML) techniques for various sub-areas in SG [11]. However, shallow neural networks and sample ML models pose many challenges that make them seldom employed for complex problems in EPSs [12], [13]. These challenges broadly lie in two facts: Firstly, the nondeep-learning algorithms are ineffective for high-dimensional representations with unreasonable complexities [14], [15].…”

Section: B Emergence Of Deep Learningmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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“…To develop a thriving security ML application, the initial step involves collecting raw signals and generating a dataset. However, this process often encounters challenges, such as inconsistencies, imputations, and redundancies, which can lead to inaccurate results [5][6][7]. Therefore, employing proper data pre-processing techniques becomes imperative in the development of any successful machine learning application [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Semi-supervised models are combinations of supervised and unsupervised models, which train the model using a small number of labeled samples and a high number of unlabeled samples [4,5], while reinforcement learning models are trained based on rewarding the desired actions or punishing undesired behaviors. In such models, the agent attempts to interpret the environment, act, and learn via trial and error [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning: Models, Challenges, and Research Directions

Talaei Khoei,

Kaabouch

2023

Future Internet

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Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and parameter optimization. While existing surveys have shed light on these techniques, they have mainly focused on specific application domains. A notable gap that exists in current studies is the lack of a comprehensive overview of machine learning architecture and its essential phases in the cybersecurity field. To address this gap, this survey provides a holistic review of current studies in machine learning, covering techniques applicable to any domain. Models are classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning. Each of these categories and their models are described. In addition, the survey discusses the current progress related to data pre-processing and hyperparameter tuning techniques. Moreover, this survey identifies and reviews the research gaps and key challenges that the cybersecurity field faces. By analyzing these gaps, we propose some promising research directions for the future. Ultimately, this survey aims to serve as a valuable resource for researchers interested in learning about machine learning, providing them with insights to foster innovation and progress across diverse application domains.

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UHF Partial Discharge Localization in Gas-Insulated Switchgears: Gradient Boosting Based Approach

Cited by 10 publications

References 15 publications

Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms

Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

Machine Learning: Models, Challenges, and Research Directions

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