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Efficient and Resilient Placement Strategy for Electric Vehicle Charging Stations, Incorporating Renewable Energy and Storage Equipment in Distribution Networks

Efficient and Resilient Placement Strategy for Electric Vehi...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Kumar, Rakesh Begam, S. Shajathi Singh, Dev Lazizovich, Tuychiev Komiljon Muthubalaji, S. Al-Farouni, Mohammed Varanasi, Srinivas GLA University Department of Computer Engineering & Applications Mathura India E.G.S. Pillay Engineering College Department of Computer Science and Business System Tamilnadu Nagapattinam India Allenhouse Institute of Technology Mathematics Department Kanpur India Tashkent State University of Economics Department of Marketing Uzbekistan CMR College of Engineering & Technology Department of Electrical and Electronics Engineering Telangana Hyderabad501401 India The Islamic University Najaf Iraq GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

The placement of Electric Vehicle Charging Stations (EVCS) is a significant obstacle to the widespread adoption of Electric Vehicles (EVs). However, integrating EVCS with distributed Renewable Energy (RE) sources may help mitigate the variability of RE production. This article focuses on studying a Microgrid (MG) that consists of power distribution sources such as Wind Turbines (WT) and photovoltaic (PV), as well as EVCSs and Energy Storage Structures (ESS). The analysis considers the ambiguities associated with the charging needs of electric vehicles and the production of distributed Renewable Energy Sources (RES). A comprehensive optimization model is provided for computing the best Charging Stations (CS) locations equipped with distributed electricity. This model combines the electrical network and the grid to ensure efficiency and reliability. The usage variation ratio is employed to assess the level of compatibility among the uncertain production curve of RE and the charging request curves to ascertain the suitable size of the WT and photovoltaic-producing method and energy storage system. The kernel prediction function is utilized to address the problem of excessive caution in adaptable optimizing methods. A simulation is conducted on an IEEE 33-nodal energy distributing system and a 50-nodal transit network. The findings from simulations provide evidence of the model's resilience and cost-effectiveness. © 2023 IEEE.

关键词： Electric power distribution

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference 27

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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27th International Conference on Information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

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The Spreading Prediction and Severity Analysis of Blood Cancer Using Scale-Invariant Feature Transform

The Spreading Prediction and Severity Analysis of Blood Canc...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Mohammed, Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Ramakrishnan, Rakesh Logeshwaran, J. Lewis University Department of Computer & Information System IL United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of the Cumberlands Department of Information Technology Union CityCA United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350300826

Blood cancer is an ever-growing global health concern. Early detection and accurate diagnosis are key to successful treatment and long-term prognosis of the disease. Despite various advances in medical technologies, current methods for blood cancer detection are far from perfect and often suffer from a lack of accuracy. This paper proposes the use of Scale-Invariant Feature Transform (SIFT) based approaches for predicting the spreading of blood cancer and assessing its severity. SIFT is a computer vision technique that extracts meaningful features from an image. Through SIFT, meaningful patterns in medical imaging datasets, such as those used for blood cancer detection, can be identified. The output from SIFT can then be used to build predictive models which can accurately predict the spread of the disease and its severity. Additionally, this paper explains how machine-learning algorithms and artificial neural networks can be used to further enhance the accuracy of these predictions. Finally, the proposed approach is validated using benchmark datasets and its accuracy is compared with existing methods for blood cancer prediction. The proposed model reached 89.71% accuracy, 89.25% precision, 90.71% recall and 89.40% f1-score results. The results show that prediction accuracy increases significantly when SIFT is used. It is concluded that SIFT can be a powerful tool for predicting blood cancer and estimating its severity. © 2023 IEEE.

关键词： Diseases

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A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

A Novel Predictive Analysis to Identify the Weather Impacts ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Mohammed, Mehmood Ali Ramakrishnan, Rakesh Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of the Cumberlands Department of Information Technology Union CityCA United States Lewis University Department of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350300826

Reinforcement learning is a powerful approach for predictive analysis to identify the weather impacts for congenital heart disease. The key advantages of this method include the utilization of sensor data to predict the weather conditions on a daily basis and the ability to learn from feedback and adapt the predictions over time. In this paper, an innovation model has proposed by using reinforcement learning algorithm. It can gain important insights regarding the impact of weather on congenital heart disease. The predictive models developed using this approach have the potential to help medical professionals as well as the general public in better predicting and managing congenital heart disease. This could help reduce costs associated with care and ultimately improve health outcomes. © 2023 IEEE.

关键词： Predictive analytics

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An Integrated Demand Side Management Framework Utilizing Smart Grid Technologies and IoT for Enhanced Residential Energy Efficiency

An Integrated Demand Side Management Framework Utilizing Sma...

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： P. Venkatesan C. Gopinath P. Prabhavathy Department of Electrical and Electronics Engineering Center for Integrated Smart Waste Management System Department of Electrical and Electronics Engineering Sri Venkateswara College of Engineering (Autonomous) Sriperumbudur India Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Increasing energy demands and the need for efficient management necessitate innovative solutions. This study introduces an integrated Demand Side Management (DSM) framework tailored for residential buildings within smart grid systems. The proposed methodology employs Earth Worm Optimization Algorithm (EWOA) and Virulence Optimization Algorithm (VOA) to optimize scheduling for household appliance using integration of Renewable Energy Storage (RES) and Renewable Energy Resources (RERs). Real-Time Electricity Scheduling (RTES) is implemented using IoT technologies to enable adaptive energy management. This system demonstrates significant performance improvements, achieving a 76.19% reduction in the Peak-to-Average Ratio (PAR) using VOA and a 73.8% reduction with EWOA when RES is included. Comparative analyses highlight substantial cost reductions, enhanced grid stability, and improved energy efficiency. This work not only showcases the novelty of integrating advanced optimization algorithms with IoT but also provides a robust approach to achieve sustainable and cost-effective energy management.

关键词： Renewable energy sources Demand side management Virulence Peak to average power ratio Energy efficiency Scheduling Real-time systems Smart grids Internet of Things Optimization

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Advanced Automated Vulnerability Scoring: Improving Performance with a Fine-Tuned BERT-CNN Model

Advanced Automated Vulnerability Scoring: Improving Performa...

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International Symposium on Telecommunications, IST

作者： Seyedeh Leili Mirtaheri Andrea Pugliese Narges Movahedkor AmirHossein Majd Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Rende Italy Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnique) Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

The ever-growing prevalence of software systems necessitates robust and efficient vulnerability assessment methods. An automated vulnerability assessment model can significantly streamline this process, providing accurate identification of vulnerability severity levels based on the given textual descriptions. This paper addresses this critical need by proposing a novel vulnerability score predictor model. Our model uses Bidirectional Encoder Representations from Transformers (BERT) and deep learning to classify vulnerability metrics accurately. We have fine-tuned the BERT tokenizer on NVD’s descriptions and used TextCNN for better performance. This approach represents a significant advancement in the field of automated vulnerability assessment, offering substantial benefits for security professionals by streamlining vulnerability identification and prioritization. The carried out experimental evaluations in comparison to various deep learning models, including LSTM, CNN, and LSTM-Attention, as well as two state-of-the-art automated vulnerability assessment baselines indicate an accuracy score of over $\mathbf{9 7 \%}$ across all vulnerability metrics.

关键词： Measurement Deep learning Accuracy Bidirectional control Predictive models Transformers Software systems Encoding Telecommunications Security

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An In-Depth Study of Cybersecurity Threats and Intelligent Broadcasting Network Topology 1

An In-Depth Study of Cybersecurity Threats and Intelligent B...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Chauhan, Usha Gupta, Manali Galgotias University Department of Electrical Electronics and Communication Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

The smart grid has stacked ICT power equipment. Smart grids use modern electrical sensors. Future adoption of smart devices and their interaction with networks may offer cyberthreats. Several smart grid subsystems may be exposed if a component doesn't respond in real time, reducing efficiency and reliability. Superannuated networks create cyber security risks. This article explores a secure cyber infrastructure, related breakthroughs, and cyber security concerns in a complex system. Cybersecurity goals, objectives, and risk assessments are complete. These papers are summarized. Prioritize security. Then, examine smart grid hacks to uncover weaknesses and their repercussions. This research designed a cyber-security plan for breaches, counterattacks, and actions. Future research is discussed. © 2022 IEEE.

关键词： Cybersecurity

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Ubi-Care: An Elderly Life Support Healthcare Framework Based on Ubiquitous Personal Online Data Stores

Ubi-Care: An Elderly Life Support Healthcare Framework Based...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Chen, Hong Zhou, Tao Wu, Bo Graduate School of Human Sciences Waseda University Saitama359-1192 Japan Daiichi Institute of Technology Department of Information and Electronics System Engineering Taito Tokyo110-0005 Japan School of Computer Science Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9781665410205

In recent years, the global aging population has intensified, leading to a sharp increase in social security benefits and caregiving costs. The elderly face greater health risks, and their behaviors often indicate signs of crises and illnesses. The rapid development of IoT technology offers new solutions for detecting abnormal signs, thereby promoting healthy aging, independent living, and social participation for the elderly. However, the diversity of IoT devices has led to the phenomenon of personal data silos. When the elderly leave their usual IoT environment, the issue of continuity in healthcare services becomes more pronounced. To address this issue, this study proposes a decentralized Ubi-Care framework. The framework aims to achieve complete separation of data and applications. It is based on the ActivityPub protocol and integrates various data from wearable devices, smart home sensors, and social networks, storing this data in a ubiquitous personal online data store (UPOD) and assigning different roles based on data type. UPOD applications support bidirectional following, allowing users to access data associated with relevant roles, addressing issues related to data categorization, sharing, privacy, and security. Additionally, this study proposes a method for utilizing complete UPOD data to perform anomaly detection based on the hidden Markov model (HMM). By effectively integrating IoT data into UPOD and enhancing data interoperability. The data-sharing model proposed in this study also facilitates elderly individuals and their family members in sharing relevant data as needed, while ensuring privacy protection. The methods proposed in this study will help prevent accidental injuries and enable early diagnosis of diseases, providing strong technical support for elderly healthcare. © 2024 IEEE.

关键词： Electronic health record

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Parallel Byzantine fault tolerance method for blockchain

Parallel Byzantine fault tolerance method for blockchain

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Dubey, Kumar Pradyot Gnanaprakasam, C.N. Khan, Ihtiram Raza Sadik, Md Shibli Al-Farhani, Liwa H. Ray, Samrat Department of Computer and Information Science Jagadguru Ram Bhadracharya Divyanga University UP Chitrakoot India Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Tamilnadu Chennai India Computer Science Department Jamia Hamdard Delhi India University of Portsmouth Portsmouth United Kingdom System Analysis Control and Information Processing Dep Academy of Engineering RUDN University Moscow Russia The Institute of Industrial Management Economics and Trade Peter The Great Saint Petersburg Polytechnic University Russia

ISBN: (纸本)9781032669663

Enterprise-focused blockchain applications that offer safe, anonymous, and immutable transactions are growing as blockchain technology matures. Traditional blockchain architecture has low performance and scalability, making it unsuitable for enterpriselevel applications with high concurrency and massive data. This paper studies and proposes a simplified Byzantine fault-tolerant (SBFT) consensus algorithm to improve consensus stage efficiency, a Task parallel smart contract model to maximise multi-core system parallel efficiency, and an improved blockchain system architecture to reflect the lightweight, low coupling nature of blockchain technology. It facilitates corporate application secondary development and the ParaChain blockchain and smart contract technology. Experimental verification compares the parallelization - based ParaChain blockchain’s TPS and scalability. PBFT-based blockchain systems have been greatly improved. © 2024 The Author(s).

关键词： Smart contract

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