Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Science in Information Technology Letters International Journal of Robotics and Control Systems
Elhindi, Mohamed
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparative Electromagnetic Performance Analysis of Double Stator and Single Stator Superconducting Generators for Direct-Drive Wind Turbines Elhindi, Mohamed; Abdalla, Modawy Adam Ali; Omar, Abdalwahab; Pranolo, Andri; Mirghani, Abdelhameed; Omer, Abduelrahman Adam
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1385

Abstract

Superconducting synchronous generators, especially for 10-MW direct-drive wind power systems, are gaining prominence due to their lightweight, compact design, lowering energy generation costs compared to conventional generators. With the ability to generate high magnetic fields. various approaches are exist for designing such generators for example modular superconducting generators which allow for easier assembly, maintenance, and scalability by dividing the generator into smaller, interchangeable components and single stator which simplifying the generator's design and reducing manufacturing costs. This study introduces a novel concept of a double-stator superconducting generator alongside a conventional single-stator superconducting generator, aiming to investigate and contrast the electromagnetic performance of both machine types considering different number pole pairs. Booth of the machines has been designed and studied applying 2d finite element model (COMSOL Multiphysics). The compared machine parameters include: the flux linkage and electromagnetic torque. Our study and compression of the two machines reveal that the double stator superconducting generator is characterized by high electromagnetic torque compared to its single-stator counterpart. the analysis also reveals that increasing the pole pairs number leads to high electromagnetic torque and higher magnetic flux density.
Machine learning-based residential load demand forecasting: Evaluating ELM, XGBoost, RF, and SVM for enhanced energy system and sustainability Abdalla, Modawy Adam Ali; Ishaga, Ahmed Mohamed; Osman, Hassan Ahmed; Elhindi, Mohamed; Ibrahim, Nasreldin; Snani, Aissa; Hamid, Gomaa Haroun Ali; Hammad, Abdallah
Science in Information Technology Letters Vol 6, No 1 (2025): May 2025
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v6i1.1866

Abstract

Accurate forecasting of electrical power load is essential for properly planning, operating, and integrating energy systems to accommodate renewables and achieve environmental sustainability. Therefore, this study introduces different machine learning (ML) methods, including support vector machines (SVM), random forests (RF), extreme learning machines (ELM), and extreme gradient boosting (XGBoost) to predict hourly electricity demand using electricity consumption and temperature data for train and test ML models. The data is processed by autocorrelation function (ACF) and cross-correlation function (CCF) to determine the appropriate lag time for the inputs. Furthermore, ML model accuracy is assessed using coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). Results show that the ELM model achieved the highest R² in both summer (0.971) and winter (0.868), outperforming the other models in accuracy R² and error reduction (MAE and RMSE). ELM also more effectively captured load fluctuations. The result of this research has applications for load demand forecasting in the proper planning and operation of the residential grid. The results help estimate load demand and provide useful guidance for residential grid planning and management by determining the best techniques for precisely estimating load demand and identifying domestic energy consumption patterns
Co-Authors Abdalla, Modawy Adam Ali Andri Pranolo Hamid, Gomaa Haroun Ali Hammad, Abdallah Ibrahim, Nasreldin Ishaga, Ahmed Mohamed Mirghani, Abdelhameed Omar, Abdalwahab Omer, Abduelrahman Adam Osman, Hassan Ahmed Snani, Aissa