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Analysis of the Temperature Dependences of Insulation Parameters for Oil High-Voltage Transformer Using the Method FDS 65

Analysis of the Temperature Dependences of Insulation Parame...

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65th IEEE Annual International Scientific Conference on Power and electrical engineering of Riga Technical University, RTUCON 2024

作者： Gutten, Miroslav Korenciak, Daniel Kanuch, Jan University of Zilina Faculty of Electrical Engineering and Information Technology Zilina Slovakia Technical University of Kosice Faculty of Electrical Engineering and Informatics Slovakia

ISBN: (纸本)9798350365771

The article discusses the analysis of how insulation parameters of oil-filled high-voltage transformers vary with temperature, using the method of frequency dielectric spectroscopy (FDS). For clearer visual comparison, two distinct frequency graphs with temperature dependencies were selected. One graph focuses on very low frequencies, up to 0.01 Hz, where the dissipation factor is higher, while the other graph covers frequencies of 0.1 Hz, 2 Hz, and 50 Hz, where the dissipation factor is lower. These frequencies are typically used for transformer insulation analysis. This analysis allows for a relatively accurate assessment of the transformer's insulation condition using the dissipation factor and capacitance through the FDS method, even at temperatures different from the original measurement conditions. © 2024 IEEE.

关键词： Oil filled transformers

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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island

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Global Energy Interconnection 2025年第2期8卷 225-239页

作者： Aripriharta Satria Adiguna Arif N.Afandi Muhammad Cahyo Bagaskoro Department of Electrical Engineering and Informatics Faculty of EngineeringState University of MalangMalang 65145Indonesia Center of Advanced Material and Renewable Energy(CAMRY UM) Indonesia Received 31 July 2024

This research examines the optimal combination of solar panel and battery capacity in hybrid systems in 11 cities on the island of Borneo,utilizing the region’s significant solar energy potential and high irradiation *** research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization(PSO)and grey wolf optimization(GWO)algorithms to maximize energy output,reduce levelised energy costs,and maximally reduce carbon *** show Tara-kan as the most optimal location,generating 215,804.88 kWh for IDR 916.9/kWh and lowering emissions by 435,884.29 kgCO_(2)e,while Samarinda is the least optimal ***,electricity tariffs of IDR 2,466.78/kWh and IDR 2,000/kWh generate a positive Net Present Value(NPV)with a payback period(PP)of 9-12 years,while a tariffof IDR 1,500/kWh is considered *** demonstrate the effectiveness of PSO and GWO in optimizing the renewable energy system and confirm the project’sfinancial viability,with a positive NPV and reasonable *** renewable energy systems in Kalimantan Island can improve energy effi-ciency and significantly reduce carbon emissions,supporting environmental sustainability goals.

关键词： Techno-economic analysis Electric vehicle Electric vehicle charger PSO GWO

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Analysis of Distribution Transformers at Various Insulating Conditions by Frequency Method 8

Analysis of Distribution Transformers at Various Insulating ...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

ISBN: (纸本)9798350354423

The study examines the insulation properties of various power transformers using method of frequency dielectric spectroscopy (FDS). It compares different transformers at the same indoor temperature and analyzes how temperature affects insulation properties. Compared to other methods of diagnosing the insulation system of high-voltage equipment, method FDS allows for separate assessment of quality and specific conductivity in oil and moisture content in the transformer solid insulation. Using capacity measurements via method FDS to determine condition of insulation system has the advantage of being less temperature-dependent compared to the dissipation factor method. However, a drawback of the capacity method is that it is influenced by the insulation geometric arrangement, which varies based on the power transformer type and performance. Given this limitation, when comparing frequency-dependent capacities to assess insulation condition, it is advisable to compare machines of the same construction and power rating. © 2024 IEEE.

关键词： Distribution transformers

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Acoustic Emission Analysis of Dry Distribution Transformers 8

Acoustic Emission Analysis of Dry Distribution Transformers

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Korenciak, Daniel Gutten, Miroslav Kanuch, Jan University of Zilina Faculty of Electrical Engineering and Information Technology Zilina Slovakia Technical University of Kosice Faculty of Electrical Engineering and Informatics Slovakia

ISBN: (纸本)9798350354423

This article primarily discusses the acoustic emission analysis method and its application in the contactless diagnostics of distribution transformers with resin dry insulation. Initially, the article outlines a measurement technique used to compare the acoustic emissions of two machines of the same type and power during their operation. A parabolic camera was employed to pinpoint the source of increased noise in the exposed transformer. This approach enables the analysis of the frequency spectrum, allowing for 3D observation of the transformer and identification of the highest noise levels and acoustic emissions. The latter part of the article details the methods for measuring and evaluating the instability and magnitude of acoustic emissions in dry transformers under laboratory conditions. The measurements were conducted using a noise measuring device equipped with a three-octave spectral filter, covering a frequency range of 100 to 10 000 Hz. © 2024 IEEE.

关键词： Distribution transformers

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Alignment of Objects in 3D Space Using Radon Transform for Cargo-Mail Scanner 66

Alignment of Objects in 3D Space Using Radon Transform for C...

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66th International Symposium ELMAR, ELMAR 2024

作者： Dobrovolny, Martin University of Pardubice Faculty of Electrical Engineering and Informatics Dept. of Electrical Engineering Pardubice Czech Republic

ISBN: (纸本)9798350375411

This paper presents the results of comparison of the proposed unique algorithm for object alignment in 3D space with the conventional approach based on Principal Component Analysis. The proposed algorithm is based on the use of Radon Transform. The algorithm has been developed for the necessity of designing a 3D scanner for large-scale Cargo-Mail in the field of air logistics. Shipments are sorted in the logistics center of the external partner and then placed in air containers. Using the proposed method, the rotation compensation of objects in the 3D scanner space was effectively solved. Thanks to the proposed method, the scanner works without manual intervention of the operator. The proposed method is robust even for situations with poorly cropped background under packages. © 2024 IEEE.

关键词： 3D scanning Data alignment Point Cloud Principal Component Analysis Radon transform

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Optimizing Feature Selection for Stock Price Prediction Using a Two-Stage Feature Selection Framework

Optimizing Feature Selection for Stock Price Prediction Usin...

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2024 Beyond Technology Summit on informatics International Conference, BTS-I2C 2024

作者： Priyatno, Arif Mudi Prasetya, Didik Dwi Widiyaningtyas, Triyanna Faculty of Engineering Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia

ISBN: (纸本)9798331508579

Stock price prediction with stock price history and technical indicators has challenges in high feature dimensions. Recursive feature elimination feature selection is one solution to overcome this. However, there are several disadvantages to recursive feature elimination. Depending on the machine learning model used, the highest feature ranking may not always be the most relevant, and it also requires lengthy computation time. This study proposes feature selection optimization for stock price prediction using a two-stage feature selection framework. Stage 1 feature selection uses a combination of minimum and maximum that are relevant to the concept of quotient and important random forest features. Stage 2 feature selection uses recursive feature elimination. The stock data used are PT Bank Central Asia Tbk (BBCA), PT Bank Mandiri (Persero) Tbk (BMRI), and PT Bank Tabungan Negara (Persero) Tbk (BBTN). The prediction results show that the RMSE in the proposed method gets the best value under 20 features, and the MAPE value in the proposed method gets a value that is always smaller than other methods. This proves that the Two-Stage Feature Selection Framework successfully optimizes feature selection for stock price prediction, thereby reducing the error rate of stock price prediction. © 2024 IEEE.

关键词： Feature Selection

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Development of a Micro Counselling Educational Platform Based on AI and Face Recognition to Prevent Students Anxiety Disorder

Development of a Micro Counselling Educational Platform Base...

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2024 International Conference on electrical and Information Technology, IEIT 2024

作者： Putri Syarifa, Dhea Fanny Krisna Moeljono, Azarya Aditya Hidayat, Wahyu Nur Shafelbilyunazra, Alvalen Abednego, Victory Kuriakos Prasetya, Didik Dwi Universitas Negeri Malang Faculty of Engineering Department of Electrical Engineering and Informatics Malang Indonesia

ISBN: (纸本)9798331516888

The rapid development of Information and Communication Technology (ICT) has brought broad consequences for example in the fields of education, health but mental health is rarely considered especially among Indonesian teenagers. Traditional approaches, such as in-person consultations with mental health professionals, often fall short due to accessibility and affordability issues. This research developed MoodNow, an E-learning system with personalized mental support using face recognition and artificial intelligence for students in grades 10 to 11 of SMK Negeri 10 Malang. Built with Design Thinking, the platform is dedicated to offering real-time mental health checks and customized virtual counseling. The system architecture incorporates user-friendly interfaces, robust backend processing, and AI-driven mood detection via the Gemini API Usability tests were also conducted, which presented a very high detection accuracy on emotions of 90%. This exchange rate gave the platform an excellent usability score, scoring 85.9% thus characterizing the project as highly effective. MoodNow's integration of AI and face recognition technologies demonstrates its potential to transform mental health support by offering real-time, personalized interventions. Despite challenges such as privacy concerns and the need for improved emotion detection accuracy, MoodNow represents a significant advancement in providing accessible and effective mental health care. This research contributes to the Sustainable Development Goals (SDG 3) by promoting mental well-being and fostering a supportive educational environment. © 2024 IEEE.

关键词： Students

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Lies Uncovered: Comparing the Performance of Deep Learning Techniques in Video-Based Deception Detection 24

Lies Uncovered: Comparing the Performance of Deep Learning T...

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2nd International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2024

作者： Yeni, Rahayu Dwi Fatichah, Chastine Yuniarti, Anny Department of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muhammadiyah Jember Indonesia

ISBN: (纸本)9798400716898

The discovery of effective and efficient deception detection techniques in video data has significant implications in various fields such as psychology and law enforcement. With the advancement of computer technology, particularly in the field of deep learning, this research aims to improve the accuracy and efficiency of deception detection in video data. Although previous research has investigated deep learning techniques for deception detection in video data, there is still a need to overcome limitations in the use of real-world datasets, generalization of models, and consideration of their efficiency and effectiveness in practical applications. This research investigated seven deep learning models, including 1D CNN, 2D CNN, 3D CNN, RNN, LSTM, GRU, and Transformer Self Attention, for deception detection in video data. The experimental data comes from real case videos that have been labelled as honest or not based on court decisions. This dataset consisted of 121 videos, of which 61 were labelled as deceptive and 60 as honest. Each video contains 1600-2400 frames, with a total of 241504 data entries processed. It was found that 1-D CNNs were the most promising for detecting deceptive video data, with an accuracy of 0.99 and a testing time of 4.63 seconds, providing a good combination of high accuracy and time effectiveness. The GRU and LSTM also show strong potential, particularly in applications where the trade-off between speed and accuracy is critical. Future research should explore modifying this deep learning method to better suit the context of deception detection by recognizing each frame as a part of a unified video. © 2024 ACM.

关键词： Video analysis

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection Autoencoder Reconstruction error Machine learning

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Verify the Effectiveness of the Intelligent Data Layer on Different Hardware Configurations 17

Verify the Effectiveness of the Intelligent Data Layer on Di...

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17th IEEE International Scientific Conference on informatics, informatics 2024

作者： Majerik, Filip Borkovcova, Monika University of Pardubice Faculty of Electrical Engineering and Informatics Pardubice Czech Republic

ISBN: (纸本)9798350387674

The article builds on research focused on the design and construction of an Intelligent Data Layer (IDL) using Object-Relational Mapping (ORM) frameworks, which can sometimes be inefficient. The aim of this article is to verify whether IDL functions as an optimization technique in different hardware environments. Previous research has shown that IDL can significantly improve the performance of applications, especially in loading and processing large amounts of data. However, this improvement was observed on standard test hardware. Therefore, we conducted this testing, which includes tests on different hardware configurations, specifically a server and a test workstation. The tests focus on data retrieval speed, memory usage efficiency, and overall application performance when using IDL compared to traditional approaches. The results of the tests will help determine whether IDL is a universal optimization technique or whether its effectiveness depends on specific hardware conditions. This knowledge is crucial for us for the future development and deployment of IDL in real-world applications. The experiments were conducted in an environment with an Nginx web server, PHP, and a MySQL database. The tested implementation runs on Symfony and Doctrine. For the individual experiments, a relational database model from previous work was used, which included tables with varying numbers of rows. © 2024 IEEE.

关键词： Relational database systems

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