Recent Advances in Electrical & Electronic Engineering - Online First
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Optimizing Distribution Transformer Ratings in the Presence of Electric Vehicle Charging and Renewable Energy Integration
Authors: Sachin Argade, Vishal Vashikar, Harsha Anantwar and Sudarshan L. ChavanAvailable online: 24 January 2025More LessAims and BackgroundA re-evaluation of distribution transformer ratings is necessary to ensure efficient and reliable operation due to the significant impact of the growing number of electric vehicles (EVs) on load dynamics. The objective of this study is to optimize the rating of the distribution transformers to accommodate the year-round demand for electric vehicle charging while minimizing expenses and no-load losses. The performance and lifespan of transformers under dynamic hourly loads are evaluated by analysing real-world EV charging data, load profiles, and transformer parameters. Using state-of-the-art simulation, real-world data analysis, and optimization algorithms, this study optimizes the transformer distribution ratings under the integration of EV charging and renewable energy.
Objectives and MethodologyDynamic hourly load changes can be captured by analysing real-world electric vehicle charging data in conjunction with seasonal load profiles. In order to maximise transformer lifetime and minimise losses, convex optimisation is subjected to Karush-Kuhn-Tucker (KKT) conditions. Transformer performance is also assessed by using energy storage devices and solar energy simulations. To determine the impact of various electric vehicle charging scenarios on thermal stress and transformer ageing, sensitivity analyses are performed.
Results and DiscussionsTransformers with a 10% higher rating can manage maximum electric vehicle charging loads with a 15% slower loss of life acceleration, according to our models. Transformer performance is further optimised with the integration of solar energy and energy storage systems, which improves load control and reduces operational costs by up to 20%.
ConclusionsUsing a convex optimisation framework and the Karush-Kuhn-Tucker (KKT) criteria, the study achieves a 95% accuracy rate in predicting hourly load fluctuations.
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A Deep Ensemble Learning Approach for Automatic AD Detection
Authors: A.G. Balamurugan and N. GomathiAvailable online: 22 January 2025More LessIntroductionEarly detection of Alzheimer's disease (AD) is crucial due to its rising prevalence and the economic burdens it imposes on individuals and society. This study aimed to propose a technique for the early detection of AD using MRI scans.
MethodThe methodology involved collecting data, preparing the data, creating both single and combined models, assessing with ADNI data, and confirming with additional datasets. The approach was chosen by comparing various scenarios. The top six individual ConvNet-based classifiers were combined to form the ensemble model. The evaluation showed high accuracy rates across various classification groups. Validation of additional data showed impressive accuracy, exceeding results from numerous previous studies and aligning with others.
ResultsAlthough ensemble methods outperformed individual models, there were no notable distinctions among different ensemble approaches. The ensemble model was constructed using the top six individual ConvNet-based classifiers in deep learning (DL), achieving high accuracy rates across various classification categories: 98.66% for Normal control | AD, 96.56% for Normal control | Early MCI, 94.41%
ConclusionEarly MCI/Late MCI, 99.96% for Late MCI | AD, 94.19% for four-way classification, and 94.93% for three-way classification. Validation results underscored the limited effectiveness of individual models in practical settings, contrasting with the promising outcomes of the ensemble method.
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WITHDRAWN: An Enhanced Segmentation Method Using a Fuzzy Clustering Technique for Colored Satellite Images
Authors: Dileep Kumar Yadav, Sudhriti Sengupta, Lavanya Sharma, Mukesh Carpenter and T.P. SinghAvailable online: 06 January 2025More LessThis article has been withdrawn at the request of the authors due to a conflict of interest among them that was not disclosed at the time of submission. After careful consideration, the publisher has determined that this undisclosed conflict compromises the integrity of the publication.
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