Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network
Hira Anum,
Muntazim Abbas Hashmi,
Muhammad Umair Shahid,
Hafiz Mudassir Munir,
Muhammad Irfan,
A.S. Veerendra,
Mohammad Kanan,
Aymen Flah
Affiliations
Hira Anum
Institute of Mathematics, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan, Pakistan; Corresponding author.
Muntazim Abbas Hashmi
Institute of Mathematics, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan, Pakistan
Muhammad Umair Shahid
Department of Electrical and Bio-medical Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan, Pakistan
Hafiz Mudassir Munir
Department of Electrical Engineering, Sukkur IBA University, Sukkur, 65200, Pakistan; Corresponding author.
Muhammad Irfan
Department of Electrical and Bio-medical Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan, Pakistan
A.S. Veerendra
Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India; Corresponding author.
Mohammad Kanan
Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia
Aymen Flah
Energy Processes Environment and Electrical Systems Unit, National Engineering School of Gabes, University of Gabes, Gabès, 6029, Tunis, Tunisia; MEU Research Unit, Middle East University, Amman, 11831, Jordan; Private Higher School of Applied Sciences and Technology of Gabes, University of Gabes, Gabès, 6029, Tunisia; ENET Centre, VSB—Technical University of Ostrava, 708 00, Ostrava, Czech Republic; Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi-feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi-feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi-feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.
