Secondary Control Techniques for Frequency Restoration in Microgrids Considering Communication Delays

Abstract

Microgrids (MGs) offer a sustainable solution for integrating distributed energy resources and allow for independent operation during grid disruptions. Although droop control is widely used for managing power distribution among generation units, it can result in system frequency and voltage deviations. Communication delays within the secondary control system can negatively impact system stability.This study evaluates and compares three secondary control techniques, Proportional-Integral (PI), Fractional-Order Proportional-Integral (FOPI), and Artificial Neural Network (ANN) based controllers, in mitigating frequency deviations in MGs with communication delays. A MATLAB/Simulink-based microgrid model incorporating three voltage source converters (VSCs) is developed to analyze the performance of these controllers under varying conditions. Results demonstrate that ANN-based control significantly outperforms PI and FOPI controllers regarding stability, response time, and robustness to communication delays as ANN controller exhibits lower peak overshoot, faster settling time, and superior adaptability, showing promise for MG secondary control. This research underscores the importance of intelligent control methods in addressing communication-induced disturbances and paving the way for future advancements in decentralized energy management.

Introduction

• The integration of renewable energy sources (RES) has led to the development of microgrids (MGs)—localized grids that enhance energy resilience and efficiency.

• Microgrids can operate in both grid-connected and islanded modes, requiring robust control mechanisms for stable operation, particularly in voltage and frequency regulation.

• Droop control is commonly used for power sharing but causes frequency deviations, necessitating Secondary Control Systems (SCS) to restore nominal values.

• Communication delays in SCSs are a critical challenge affecting system stability.

Objective:

To evaluate and compare three secondary control strategies—Proportional-Integral (PI), Fractional-Order PI (FOPI), and Artificial Neural Network (ANN)-based controllers—for frequency restoration in MGs, particularly under communication delays.

Key Concepts and Technologies:

1. Hierarchical Control Structure:

• Primary Control: Local droop control and converter regulation; fast response but introduces frequency/voltage deviations.

• Secondary Control: Restores nominal frequency and voltage via centralized, distributed, or decentralized control methods.

• Tertiary Control: Optimizes economic dispatch, grid synchronization, and load forecasting.

2. Communication Delay Impact:

• Delays cause frequency instability, oscillations, and slow system response.

• Mathematical model: G(s)=K1+sTe−sTG(s) = \frac{K}{1+sT}e^{-sT}G(s)=1+sTK?e−sT

• ANN and Model Predictive Control (MPC) can mitigate delay impacts.

Control Strategies Evaluated:

1. Proportional-Integral (PI) Controller:

• Simple and widely used.

• Performance degrades under variable loads and communication delays.

2. Fractional-Order PI (FOPI) Controller:

• Incorporates fractional calculus to improve adaptability and transient response.

• Better than PI but still vulnerable to long delays.

3. Artificial Neural Network (ANN) Controller:

• Utilizes a Multi-Layer Perceptron (MLP) trained via backpropagation.

• Learns system dynamics and auto-tunes for minimal overshoot and fast settling time.

• Most robust under varying operating conditions and communication delays.

Methodology:

• Model Development: A MATLAB/Simulink-based microgrid with solar PV, DC-DC converters, and three VSCs.

• Simulation Scenarios:

  • Nominal (no delay)

  • Delays of 0.1s, 0.2s, 0.3s

  • Load variations

• Performance Metrics:

  • Settling Time (Ts)

  • Overshoot (Mp)

  • Robustness against delays

Results:

• ANN-based controllers showed the best performance:

  • Shortest settling time

  • Lowest overshoot

  • High robustness to communication delays

• FOPI outperformed PI but still struggled with long delays.

• ANN’s adaptive learning made it superior in dynamic and uncertain conditions.

Conclusion

Considering communication delays and system uncertainties, this study analyzed the effectiveness of PI, FOPI, and ANN-based secondary control strategies for microgrid frequency stabilization. The simulation results demonstrated that ANN-based control significantly outperforms traditional controllers in terms of response time, stability, and robustness under varying communication conditions. Key findings include: 1) PI Controller: Exhibited the longest settling time (4.5s) and highest overshoot (8.2%), making it less effective under communication delays. 2) FOPI Controller: Improved dynamic response with a settling time of 3.2s and overshoot of 6.5%, but became unstable beyond 0.3s delay. 3) ANN Controller: Demonstrated the fastest response (settling time: 1.8s, overshoot: 3.1%), proving highly adaptive even under high communication delays. The ANN-based secondary control method ensures superior frequency restoration, minimized oscillations, and enhanced system stability, making it the most effective strategy for modern microgrids.

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Copyright

Copyright © 2025 S Shiva Raj, K V Sharma, M T Naik . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.