Energy Management Control of an Autonomous Hybrid System Based on ANN and Golden Jackel Algorithm | ||||
| Engineering Research Journal (Shoubra) | ||||
| Volume 53, Issue 4, October 2024, Page 125-132 PDF (1.08 MB) | ||||
| Document Type: Research articles | ||||
| DOI: 10.21608/erjsh.2024.300240.1326 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Dina Mustafa Abdelrahman  1; Ahmed Hussein Mustafa2; Islam Ahmed Abdelqawee2; Mahmoud Alahmar2
| ||||
| 1Department of Electrical Engineering, Faculty of Engineering, Helwan University, Cairo, Egypt | ||||
| 2Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt. | ||||
| Abstract | ||||
| This study presents a self-sufficient hybrid solar and wind energy system for a community area that the utility grid may have difficulty providing. This system, merging solar panels and a wind turbine with batteries for energy storage, which stores the excess energy from the PV and wind systems and supplies it in case of deficiency of the power, employs an artificial neural network (ANN) and proportional-integral (PI) controller to track the maximum power point under varying weather conditions. The ANN makes the system more efficient with less energy losses, while the control system's accuracy is improved by the PI controller, enhanced by the golden jackal optimization algorithm (GJO) to get the best parameters for optimal performance and reduce the error as much as possible. The results are compared with the particle swarm optimization (PSO) algorithm. The proposed system is designed using HOMER software, and the system simulations are implemented using the MATLAB/Simulink package. | ||||
| Keywords | ||||
| Hybrid PV/Wind; Artificial Neural Network; Maximum Power Point Tracking; Golden Jackel Optimization algorithm | ||||
|
Statistics Article View: 145 PDF Download: 54 |
||||
