1,720,967 research outputs found
PRPD verilerini ve derin öğrenme tekniklerini kullanarak kısmi boşalma kaynağı tanımlama
No full textPartial discharge is a phenomenon of localized electrical discharge that only partially
bridges the insulation between conductors and which can or cannot occur adjacent
to a conductor. It can occur within cables, transformers, and electrical equipment
under high-voltage stress. As it is a dangerous event that progresses rapidly, it
eventually causes the insulation materials to be damaged and the equipment to be
out of service. In this thesis, the features of the Partial Discharge phenomenon are
investigated. The studies on this field are revealed with the literature research. Test
setups similar to real-life situations are constructed in the laboratory environment,
and experiments are carried out related to corona, internal, and surface partial
discharges. Grayscale images are created using PRPD data obtained from laboratory
experiments, and they are used to train and test the deep-learning models for the
classification of the partial discharge types. For this purpose, ResNet50, VGG16,
VGG19, Inception, and Xception models are used via the transfer learning method.
According to the results, the deep learning models have reached high accuracy levels.Kısmi Boşalma, iletkenler arasındaki yalıtımı yalnızca kısmen köprüleyen ve bir
iletkenin yanındayken veya değilken meydana gelebilecek olan bölgesel elektrik
boşalması durumudur. Bu durum, yüksek gerilim stresi altındaki kablolarda,
trafolarda ve elektrikli ekipmanlarda oluşabilir. Hızla ilerleyen tehlikeli bir olay
olduğu için sonunda yalıtım malzemelerinin zarar görmesine ve ekipmanların hizmet
dışı kalmasına neden olur. Bu alanda yapılan çalışmalar literatür araştırması ile
ortaya konulmuştur. Laboratuvar ortamında gerçek hayattaki durumlara benzer test
düzenekleri kurularak korona, iç ve yüzey kısmi boşalmaları ile ilgili deneyler
gerçekleştirilmektedir. Laboratuvar deneylerinden elde edilen PRPD verileri
kullanılarak gri tonlamalı görüntüler oluşturulmuş ve bunlar kısmi deşarj türlerinin
sınıflandırılması için derin öğrenme modellerini eğitmek ve test etmek için
kullanılmaktadır. Bu amaçla, öğrenme aktarımı yöntemi ile ResNet50, VGG16,
VGG19, Inception ve Xception modelleri kullanılmıştır. Elde edilen sonuçlara göre
derin öğrenme modelleri yüksek doğruluk seviyelerine ulaşmıştır.M.S. - Master of Scienc
Nanocomposite Based Insulation Systems: A Review of Materials and Techniques for High Voltage Applications
No full textCompared to conventional insulation materials, nanocomposite (NC)-based insulation systems represent novel progress in high voltage (HV) systems, offering superior electrical, thermal, and mechanical properties. This review comprehensively analyzes the materials and fabrication methods used to develop NC insulation systems with a well-defined application, such as energy storage devices, power transmission lines, transformers, and capacitors. Nanoparticles (NPs) such as carbon nanotubes (CNTs), graphene, alumina, and boron nitride (BN) can enhance dielectric breakdown strength, mechanical robustness, and thermal conductivity. NCs offer reduced dielectric loss and adjustable permittivity, making them ideal candidates for energy storage and capacitive applications. However, some challenges remain in the large-scale fabrication of NC insulation systems. Cost considerations, controlling filler-matrix interactions, preventing nanoparticle agglomeration, achieving uniform nanoparticle dispersion within the polymer matrix, and scaling up production are key issues. Agglomeration, which leads to uneven nanoparticle distribution, negatively affects the material’s properties and performance, making it one of the major tasks to solve for improving NC systems. Developing biodegradable and recyclable NCs and exploring new nanomaterials are the future perspectives of hybrid insulation systems. This progress could result in more sustainable, multifunctional insulation materials and efficient systems for next-generation high-voltage applications. This review outlines both the current state and prospects of NC insulation systems in power systems
Numerical and Experimental Evaluation of Indirect Cold Atmospheric Plasma for Breast Cancer Treatment
No full textEffective cancer therapy relies on the precise targeting and elimination of malignant cells while preserving the integrity of surrounding healthy tissue. Cold atmospheric plasma (CAP) has emerged as a promising treatment modality, yet its therapeutic efficacy remains incompletely characterized. In this study, we present a comprehensive and quantitative framework to evaluate CAP–tissue interactions using breast tissue-mimicking phantoms, combining experimental dielectric characterization with simulation-based plasma modeling. In addition, we introduce a novel method to assess CAP–tissue interactions through the reflection coefficient (S11), enabling detailed characterization of frequency-resolved dielectric responses. Numerical simulations estimate electron densities on the order of 1016 (1/m3), while experimental investigations employ a plasma jet driven by a custom-designed circuit. Voltage and current measurements are analyzed alongside simulation data to estimate electron density, and S11 measurements are used to extract impedance and permittivity changes. The results consistently demonstrate that CAP exposure increases S11 and impedance while reducing dielectric properties, with effects strongly dependent on input voltage, electrode distance, and treatment duration. This phantom-based framework provides a reproducible and quantitative approach to evaluate CAP effects, establishing a solid foundation for future ex-vivo studies and enabling controlled, noninvasive monitoring of plasma-induced dielectric changes in biologically relevant models
Adversarial Partial Discharge Signal Reconstruction and Denoising with an Encoder-Decoder Network
No full textAs the application of online partial discharge (PD) measurement increases the importance of denoising becomes more and more obvious. Besides denoising a PD signal to detect and calculate discharge amplitude, pulse reconstructing is required to compute rise time, fall time and other features which play a crucial role in determining discharge severity and identifying defect types. In this paper, a deep learning method based on adversarial deep network by using encoder-decoder network as a generator is developed to perform a denoising and signal reconstruction task. The issue is to provide a database that contains both noisy and denoised data because every denoising method has its limitation, and it is not possible to train a network with real data and their perfect denoised pulses. Therefore, a synthetic database is developed, and signal deformation and noise added synthetically. The trained network’s performance is evaluated under actual conditions in two distinct laboratories on various days, with differing noise levels and waveforms. The proposed method outperforms the wavelet method in denoising synthetic data and shows an improvement on real data, while successfully reconstructing the PD pulses. Enhancing the network’s performance further, it underwent fine-tuning with actual noise, which led to a marked enhancement in its denoising ability and overall capabilities
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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