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Related Experiment Video

Updated: May 27, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Design of a robust neural network-based controller for frequency stability in microgrids.

Montaser Abdelsattar¹, Ibrahim A Khalaf^2,3, Alaaeldien Hassan²

¹Electrical Engineering Department, Faculty of Engineering, Qena University, Qena, 83523, Egypt. Montaser.A.Elsattar@eng.svu.edu.eg.

Scientific Reports

|May 25, 2026

Summary

A new multi-layer feedforward neural network (MLFFNN) controller enhances frequency stability in islanded microgrids (MGs) with renewable energy sources (RESs). Combining MLFFNN with virtual inertia (VI) significantly reduces frequency deviations caused by RES fluctuations and load variations.

Keywords:

Frequency stability Microgrids Multi-layer feedforward neural network Proportional-integral-derivative-acceleration controller Virtual inertia

Related Experiment Videos

Last Updated: May 27, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

Electrical Engineering
Control Systems
Renewable Energy Integration

Background:

Microgrids (MGs) integrating renewable energy sources (RESs) like solar and wind face stability challenges due to the loss of mechanical inertia from synchronous generators.
Maintaining frequency stability in islanded MGs is crucial for reliable power supply.

Purpose of the Study:

To introduce a novel controlling technique for enhancing frequency stability in islanded MGs.
To evaluate the effectiveness of a multi-layer feedforward neural network (MLFFNN) controller against traditional methods.

Main Methods:

A multi-layer feedforward neural network (MLFFNN) controller was developed and implemented.
The MLFFNN controller's performance was compared with Proportional-Integral-Derivative-Acceleration (PIDA), Proportional-Integral-Derivative (PID), and Virtual Inertia (VI) controllers.
Three distinct scenarios were simulated: load variations, RES fluctuations, and a combined case.

Main Results:

The MLFFNN controller combined with VI demonstrated superior performance in all tested scenarios.
In the combined scenario, the MLFFNN with VI reduced maximum frequency deviation from 4.92 Hz to 3.86 Hz compared to the uncontrolled system.
The proposed MLFFNN-VI controller significantly improved frequency stability over standalone VI, PID, and PIDA controllers.

Conclusions:

The MLFFNN controller, particularly when combined with VI, offers a robust solution for improving frequency stability in RES-based islanded MGs.
This advanced control strategy effectively mitigates disturbances arising from RES variability and load changes.
The findings highlight the potential of AI-driven control techniques for enhancing the reliability of future microgrid systems.