Robust Detection of Microgrid Islanding Events Under Diverse Operating Conditions Using RVFLN

Yahya Akıl (), Ali Rıfat Boynuegri and Musa Yilmaz
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Yahya Akıl: Department of Electric Power and Energy System, Dicle University, Diyarbakır 21280, Turkey
Ali Rıfat Boynuegri: Electrical Engineering Department, Faculty of Engineering, Yıldız Technical University, Istanbul 21280, Turkey
Musa Yilmaz: Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Batman University, City Center, Batman 72000, Turkey

Energies, 2025, vol. 18, issue 17, 1-25

Abstract: Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks.

Keywords: islanding detection; random vector functional link network (RVFLN); microgrid monitoring; feature engineering; robust classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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