Browsing by Author "Hossain, Md Ismail"

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Advanced Control Strategy for Mitigating Imbalances in Centralized Microgrids Using Grid-Forming Converters
(IEEE, 2025-03-14) Hannan, Nasif; Hossain, Md Ismail; Shufian, Abu; Alam, Sadman Shahriar; Shovon, S M Tanvir Hassan; Sheikh, Protik Parvez
Power instability in centralized microgrids remains a critical challenge due to fluctuating load demands and the dynamic nature of renewable energy sources. Traditional voltage and frequency control techniques often fail to tackle imbalances arising from uneven load distribution and dynamic generation patterns. To overcome this, the study introduces an advanced control strategy incorporating a novel centralized secondary control mechanism. This mechanism optimizes power management by factoring in intermittent levels, load-effective impedance, and voltage fluctuations, ensuring efficient voltage regulation and stability. The proposed approach aims to improve overall microgrid performance by dynamically balancing power distribution and enhancing control parameters. The effectiveness of the strategy is validated through simulation and experimental methods, demonstrating its ability to stabilize voltage and address power imbalances in centralized microgrids. The findings provide valuable insights for improving the reliability and efficiency of modern energy systems, particularly in dealing with voltage instability and variable load conditions in microgrid applications.
Advanced Control Strategy for Mitigating Imbalances in Centralized Microgrids Using Grid-Forming Converters
(IEEE, 2025-03-14) Hannan, Nasif; Hossain, Md Ismail; Shufian, Abu; Shahriar Alam, Sadman; Shovon, S M Tanvir Hassan; Sheikh, Protik Parvez
Power instability in centralized microgrids remains a critical challenge due to fluctuating load demands and the dynamic nature of renewable energy sources. Traditional voltage and frequency control techniques often fail to tackle imbalances arising from uneven load distribution and dynamic generation patterns. To overcome this, the study introduces an advanced control strategy incorporating a novel centralized secondary control mechanism. This mechanism optimizes power management by factoring in intermittent levels, load-effective impedance, and voltage fluctuations, ensuring efficient voltage regulation and stability. The proposed approach aims to improve overall microgrid performance by dynamically balancing power distribution and enhancing control parameters. The effectiveness of the strategy is validated through simulation and experimental methods, demonstrating its ability to stabilize voltage and address power imbalances in centralized microgrids. The findings provide valuable insights for improving the reliability and efficiency of modern energy systems, particularly in dealing with voltage instability and variable load conditions in microgrid applications.
Prediction of Typhoid Using Machine Learning and ANN Prior to Clinical Test
(IEEE, 2023-05-24) Bhuiyan, Md. Atik; Rad, Sharaf Shahariare; Johora, Fatema Tuj; Islam, Abdullah; Hossain, Md Ismail; Khan, Aliza Ahmed
One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy.