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Artificial neural network based hierarchical intelligent control framework for a residential microgrid.

Mohammed O Bahabri1, Sreerama Kumar Ramdas2, Hussam A Banawi2

  • 1Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia. mbahabri0018@stu.kau.edu.sa.

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This study introduces an artificial neural network control framework for renewable microgrids, enhancing energy harvesting and stability for sustainable residential power in Saudi Arabia.

Keywords:
Energy management systemHybrid microgridMATLAB/SimulinkNeural network controlSaudi Arabia

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Renewable Energy Systems

Background:

  • Saudi Arabia's Vision 2030 drives the need for intelligent, sustainable energy solutions.
  • Advanced control is crucial for the reliability of hybrid microgrids integrating solar and wind power.
  • Residential microgrids require robust management for fluctuating loads and energy sources.

Purpose of the Study:

  • To propose and validate an artificial neural network-based hierarchical intelligent control framework.
  • To optimize the performance of a hybrid microgrid powering a residential villa in Jeddah.
  • To enhance energy harvesting, battery management, and overall system stability.

Main Methods:

  • Development of a hierarchical control framework using artificial neural networks (MLP, NARMA-L2) and an intelligent Energy Management System (EMS).
  • Integration of a 36 kW solar PV array, 10 kW wind turbine, and 100 kWh battery.
  • Simulations in MATLAB/Simulink under realistic Jeddah environmental conditions (irradiance, wind speed).

Main Results:

  • MLP-based Maximum Power Point Tracking (MPPT) controllers improved power tracking by up to 12%.
  • NARMA-L2 battery management reduced DC bus voltage error by ~10% and improved recovery time by >5% compared to PI control.
  • The intelligent EMS demonstrated robust real-time load balancing and optimized battery operation across dynamic load scenarios.

Conclusions:

  • The proposed neural network-based hierarchical control system is effective, resilient, and scalable.
  • It offers a promising solution for smart residential microgrids in challenging coastal environments.
  • The framework significantly enhances the performance of fully renewable hybrid microgrids.