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Classification and fault diagnosis of power transformers with dissolved gas analysis using improved clustering methods

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第1期44卷 50-66页

作者： Kianimehr, Nasser Zeinoddini-Meymand, Hamed Shahnia, Farhad Grad Univ Adv Technol Dept Elect & Comp Engn Kerman Iran Murdoch Univ Sch Engn & Energy Perth Australia

PurposePower transformers are vital components of an electrical network. A defective transformer can cause instability and blackouts in parts of the network. An accurate classification of different transformer faults results in a relatively accurate fault diagnosis and timely corrective actions. It is possible to increase productivity and reduce costs by using fault detection of power transformers through the analysis of gases dissolved in oil. The proposed technique is a suitable tool to help the utilities and engineers in charge of preventive maintenance by reducing the costs of different fault diagnosis tests for power ***/methodology/approachIn this paper, the IEC 60599 standard along with clustering and classification methods are used to classify power transformer's fault types. K-means and Fuzzy C-means clustering methods are used for clustering, and the support vector machine (SVM) method is used for classification of different types of faults in power transformers. The performance of K-means and SVM methods is improved by using the Grasshopper Optimization Algorithm (GOA). The efficiency of the proposed methods is evaluated using real field data of power transformers. The purpose of this study is to propose hybrid methods including K-means-GOA clustering and SVM-GOA classification for accurate fault diagnosis. These methods have been used for the first time in fault diagnosis determination of power transformers through gas analysis. The Silhouette criteria is used in this paper to compare the efficiency of different clustering *** results of the paper are based on the gas chromatography data related to 266 different real power transformers. They show the high accuracy and high-performance speed of intelligent clustering and classification methods compared to conventional ones. This analysis would be helpful in performing the required maintenance check and plan for ***/valueThe applicability and efficienc

关键词： Dissolved gas analysis (DGA) Power transformer Insulation oil Classification Fault diagnosis clustering methods K-means-GOA SVM-GOA

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Enhancing Segmentation: A Comparative Study of clustering methods

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IEEE ACCESS 2025年 13卷 47418-47439页

作者： Ling, Lew Sook Weiling, Claireta Tang Multimedia Univ Fac Informat Sci & Technol Melaka 75450 Malaysia

With the increasing complexity of consumer preferences and behaviors, businesses face challenges to capture the dynamic nature of online consumer behavior, highlighting the need for advanced approaches. This study aims to enhance customer segmentation in e-marketing by analyzing and comparing various machine learning-based clustering methods, with a particular focus on unsupervised clustering techniques for predicting Customer Lifetime Value (CLV). While prior research has utilized unsupervised clustering for customer segmentation, this current study uniquely integrates K-Means++ with other clustering techniques to enhance segmentation accuracy and gain deeper insights into consumer behavior. This study adopts a structured, unsupervised clustering approach, enabling natural customer groupings without predefined labels, which is particularly suitable for customer segmentation in scenarios with limited labeled data. Several clustering techniques are investigated, including K-Means, K-Medoids, Agglomerative clustering, DBSCAN, Fuzzy C-Means, K-Means++, Mini Batch K-Means, Mean Shift, and Gaussian Mixture Models (GMM). K-Means++ demonstrated superior performance in segmentation accuracy, outperforming other techniques under various conditions. Performance is evaluated using key metrics such as the Silhouette Score and Davies-Bouldin Index. Utilizing Kaggle datasets, the analysis follows a comprehensive preprocessing protocol comprising RFM (Recency, Frequency, Monetary) analysis, outlier removal, and data normalization to ensure data integrity and facilitate systematic identification of distinct consumer segments. This research highlights the potential and significance of machine learning in refining customer segmentation processes within e-marketing, ultimately aiding businesses in optimizing their marketing effectiveness and strategic planning. While focusing primarily on a limited selection of clustering methods, the study underscores the necessity for ongoing explor

关键词： Business clustering methods Companies Consumer behavior Accuracy Systematic literature review Image segmentation clustering algorithms Internet Indexes Customer segmentation clustering RFM K-Means plus plus K-Medoids CLV prediction

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A Comparative Study of Various clustering methods on Dengue Microarray Gene Expression Dataset and Pathway Enrichment Analysis

A Comparative Study of Various Clustering Methods on Dengue ...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Epsita Das Piyali Chatterjee Department of Computer Science and Engineering Netaji Subhash Engineering College Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Recently, disease progression caused by dengue virus has intensely increased and affected almost 80 countries of WHO. As a result, in the year of 2023, 6.5 million cases of dengue infection followed by 7300 dengue-related deaths have been reported. The global spread of dengue is driven by the lack of vaccines and insufficient vector control efforts. Human and dengue virus interaction affects gene expression value which needs to be investigated for biomarker prediction. Using clustering methods, it is possible to identify genes contributing to a deeper understanding to know how the virus manipulates host cell machinery and gain insight of the same biological pathways or cellular processes. clustering results may help in identification of biomarkers, uncovering potential therapeutic targets. This study aims to analyze dengue gene expression dataset GSE51808 through various unsupervised clustering algorithms leveraging the identification of new biomarkers through enrichment analysis.

关键词： Drugs clustering methods Supervised learning clustering algorithms Vaccines Gene expression Machinery Intelligent systems Viruses (medical) Machine vector control

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Cross-Organization Single-cell RNA-seq Data clustering methods

Cross-Organization Single-cell RNA-seq Data Clustering Metho...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Dong Liang Xiaoshu Zhu School of Computer and Information Nanning Normal University Nanning China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Single-cell transcriptome sequencing technology provides a new way to understand cell heterogeneity and functional diversity. Batch effect correction and clustering are important steps in cross-tissue single-cell transcriptome data analysis. Choosing the right method has a great impact on downstream analysis such as cross-tissue cell type identification. First, this paper describes five typical batch effect correction methods and nine single-cell clustering methods, and analyzes their advantages and disadvantages. Then, a comparative experimental analysis is conducted on the mouse single-cell transcriptome atlas dataset Tabula Mûris. The performance comparison experiments and analysis of cross-tissue single-cell transcriptome data batch effect correction methods and clustering methods are performed using evaluation indicators such as accuracy, adjusted Rand index, normalized mutual information, and Davidson-Bourdin index. The experimental results show that among the batch effect correction methods, the clustering performance after ComBat batch effect correction is the best, and the accuracy, adjusted Rand index, normalized mutual information, and Davidson-Bourdin index after clustering have obvious advantages over other batch effect correction methods. Among the clustering methods, scDCC and scCCL have the best overall performance in terms of accuracy, adjusted Rand index, normalized mutual information, and Davidson-Bourdin index, with balanced performance and short time consumption.

关键词： Measurement Sequential analysis Accuracy clustering methods Transcriptomics Information processing Indexes Gene expression Mutual information Intelligent control

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Comprehensive Analysis of clustering methods: Focusing on Fuzzy clustering

Comprehensive Analysis of Clustering Methods: Focusing on Fu...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Vaishali P. Salve Magan P. Ghatule Dept. of Electronic Science Sinhgad College Of Science Pune India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The objective of clustering algorithms is to group or cluster data according to the underlying patterns and commonalities in the data. They are crucial to many aspects of modern life, including social media, healthcare, marketing and e-commerce, and data organization and analysis. There are many clustering methods available, and new ones are always being developed. There are strengths and limitations associated with each algorithm, and there is currently no one method that works for every task. In this study, the researchers analyzed existing clustering algorithms and categorized popular algorithms along five dimensions: fundamental various clustering techniques, fuzzy clustering, the proposed Fuzzy C-Mean (FCM) clustering, algorithms are classified based on fundamental clustering method and data size, and it also offers a comparison of studies comparing Fuzzy C-Means (FCM) clustering with other traditional clustering algorithms. This classification makes it easier for researchers to understand clustering algorithms from different angles and helps in the identification of algorithms that are appropriate for resolving particular problems. Lastly, they discussed the current condition of clustering algorithms now and possible future developments. The researchers additionally discussed and noted the field's open difficulties and unresolved issues.

关键词： Social networking (online) clustering methods clustering algorithms Focusing Organizations Medical services Classification algorithms Electronic commerce Collaborative intelligence

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clustering methods for large scale geometrical global optimization

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OPTIMIZATION methods & SOFTWARE 2019年第5期34卷 1099-1122页

作者： Bagattini, Francesco Schoen, Fabio Tigli, Luca Univ Firenze DINFO Florence Italy

In this paper we show that for some problem classes it is possible to design Global Optimization algorithms which mimic existing procedures obtaining the same quality at a fraction of their computational cost. We achieved this applying clustering methods to identify regions of attraction of local minima. If we could identify the shape of regions of attraction, a local search starting from each of them would lead to the global minimum. This idea had been a winning one in the 1980s, and later abandoned when large dimensional global optimization problems were used to test global optimization algorithms. In this paper we show that by using the idea of clustering in a feature space of much smaller dimension than the original one, very significant speed ups can be obtained. We apply this idea to two of the most widely studied families of hard, large scale, global optimization problems: the optimization of the potential energy of atomic clusters, and the problem of packing identical spheres of largest radius in the unit hypercube. We could even improve some existing putative optima, thus proving that the proposed method is not only very efficient but also effective in exploring the feasible space.

关键词： Global optimization clustering methods multi level single linkage feature space Lennard-Jones clusters Morse clusters sphere packing

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clustering methods for statistical downscaling in short-range weather forecasts

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MONTHLY WEATHER REVIEW 2004年第9期132卷 2169-2183页

作者： Gutiérrez, JM Cofiño, AS Cano, R Rodríguez, MA Univ Cantabria ETSI CAminos Dept Matemat Aplicada E-39005 Santander Spain CMT CAS Inst Nacl Meteorol Santander Spain CSIC Inst Fis Cantabria Santander Spain

In this paper an application of clustering algorithms for statistical downscaling in short-range weather forecasts is presented. The advantages of this technique compared with standard nearest-neighbors analog methods are described both in terms of computational efficiency and forecast skill. Some validation results of daily precipitation and maximum wind speed operative downscaling (lead time 1-5 days) on a network of 100 stations in the Iberian Peninsula are reported for the period 1998-99. These results indicate that the weighting clustering method introduced in this paper clearly outperforms standard analog techniques for infrequent, or extreme, events (precipitation > 20 mm;wind > 80 km h(-1)). Outputs of an operative circulation model on different local-area or large-scale grids are considered to characterize the atmospheric circulation patterns, and the skill of both alternatives is compared.

关键词： weather forecasting circulation patterns clustering methods Nearest neighbor Computational efficiency Iberian Peninsula clustering algorithms extreme wind speed

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clustering methods for Particle Filters With Gaussian Mixture Models

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第2期58卷 1109-1118页

作者： Yun, Sehyun Zanetti, Renato Univ Texas Austin Austin TX 78712 USA

New clustering methods are proposed to develop novel particle filters with Gaussian mixture models (PFGMM). In the PFGMM, the propagated samples are clustered to recover a Gaussian mixture model (GMM) using a clustering algorithm, which plays a fundamental role in the filter's performance. Two clustering methods are introduced that simultaneously minimize the covariance of each of the GMM components and maximize the likelihood function. Under the scenarios considered in this article, it is shown through numerical simulation that the PFGMMs with the proposed clustering algorithms lead to better performance than the PFGMM employing the K-means or the expectation-maximization algorithms as well as the regularized particle filter (PF) and the Gaussian sum PF.

关键词： clustering algorithms Heuristic algorithms Standards clustering methods Linear programming Covariance matrices Time measurement

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clustering methods Applied for Gene Expression Data: A Study 2

Clustering Methods Applied for Gene Expression Data: A Study

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2nd International Conference on Computational Intelligence & Communication Technology (CICT)

作者： Gupta, Shelly Singh, Shailender Narayan Kumar, Dharminder Amity Univ ASET Noida India GJUS&T CS Dept Hisar Haryana India

ISBN: (纸本)9781509002108

In bioinformatics research, the biggest challenge is to extract information from large datasets according to ones interestingness criteria. For research in genetics the DNA microarray technology provides better results in comparison with the standard approach as it has computerized the parallel analysis of thousands of genes for monitoring expression levels of genes. Thus, for the biologists the challenge to analyze gene data which consists of a huge number of measurements is in the range of clustering techniques. For the above purpose till now various clustering techniques have been developed and applied on gene microarray data. Thus, this study paper is aimed to provide a brief review of the various research papers and journals on recent research done for cluster analysis of gene microarray data.

关键词： clustering methods Microarray technology Gene expression data Cluster validation

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clustering methods for Occupancy Prediction in Smart Home Control

Clustering Methods for Occupancy Prediction in Smart Home Co...

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20th IEEE International Symposium on Industrial Electronics (ISIE)

作者： Vazquez, Felix Iglesias Kastner, Wolfgang Vienna Univ Technol Automat Syst Grp A-1040 Vienna Austria

ISBN: (纸本)9781424493128

clustering methods are deployed to extract patterns from large amounts of data. For home and building automation, usage patterns and their resulting profiles allow improving control systems with prediction capabilities. This paper shows how different clustering methods identify patterns representing the occupancy of inhabitants. Regarding the occupancy, the clustering methods are tested with real data from three kinds of rooms taken from a database of buildings monitored for five years. Later on, they are analyzed and compared using a simulated environment for the automated control of a use case dedicated to heating setpoint temperature control. As will be shown, methods based on Fuzzy C-means and eXclusive Self-Organizing Maps obtain the best performance in simulations, presenting excellent features for the application of interest.

关键词： Atmospheric modeling Buildings clustering algorithms clustering methods Heating Pattern matching Static VAr compensators

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