Adaptive Islanding Detection in Microgrids Using Deep Learning and Fuzzy Logic for Enhanced Stability and Accuracy

Document Type : Research paper

Authors

1 Kimyo International University in Tashkent, Uzbekistan.

2 Termez State University, Termez, Uzbekistan

3 Samarkand Institute of Economics and Service, Samarkand, Uzbekistan

4 Samarkand State University Named after Sharof Rashidov, University Boulevard, Samarkand, Uzbekistan

5 Shahrisabz State Pedagogical Institute, Shakhrisabz, Kashkadarya, Uzbekistan

6 Department of Software Engineering and the Digital Economy, Nukus Innovation Institute, Uzbekistan

7 International School of Finance and Technology, Tashkent Region, Kibrai District, University Street, Tashkent, Uzbekistan

8 Navoi State Pedagogical Institute, Uzbekistan

9 Tashkent State University of Economics, Tashkent, Uzbekistan

Abstract

The growing complexity of microgrid operations, driven by the integration of renewable energy sources and distributed generation, has heightened the need for more advanced islanding detection methods. Traditional techniques, such as passive and active methods, often struggle with accuracy in these dynamic environments. Passive methods can result in high false detection rates as they rely on system parameters like voltage and frequency, which are sensitive to fluctuations. Active methods, while generally more accurate, can introduce disturbances into the system and are often less effective in low-power scenarios. These limitations pose significant challenges to maintaining the stability and integrity of microgrids, underscoring the need for innovative approaches. To address these challenges, this paper presents a novel approach that combines deep learning with fuzzy logic for adaptive control in microgrids. Deep learning facilitates precise real-time data analysis, enabling the system to accurately detect islanding events as they occur. Meanwhile, fuzzy logic provides adaptable decision-making, allowing the system to respond effectively to changing conditions. This integration significantly enhances detection accuracy and reduces error rates compared to traditional techniques, ensuring reliable performance throughout the day. By offering a more robust and flexible solution, the proposed method not only improves fault detection but also enhances overall system stability, making it a valuable contribution to microgrid management. This approach addresses the critical need for more effective islanding detection in increasingly complex microgrid environments, paving the way for more resilient and reliable energy systems.

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References

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Journal of Operation and Automation in Power Engineering
Volume 12, Special Issue (Open)
Advanced Technologies for Resilient and Efficient Microgrid Management: Innovations in Energy Optimization, Security, and Integration
2024

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History

  • Receive Date: 08 November 2024
  • Revise Date: 12 January 2025
  • Accept Date: 15 January 2025
  • First Publish Date: 15 January 2025

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