Document Type : Original Article

Authors

• Fereshteh poorahangryan ¹
• Masoumeh seyedi ²

¹ Assistant Professor, Department of Electrical Engineering Ayandegan University, Tonekabon, Iran

² Assistant Professor, Department of Electrical Engineering, Ayandegan University, Tonekabon, Iran

Abstract

This study presents an islanding detection strategy for inverter interfaced distributed generation wherein the detection is governed by a learning-based characterization of load. In contrast to conventional frequency-based relays, the proposed approach deliberately introduces a controlled reactive power imbalance to induce a measurable frequency deviation while adaptively tuning its response according to inherent load attributes, including the resonant frequency, the quality factor, and robustness against non-Gaussian load achieved through Gaussian Model (GMM) clustering. To identify these characteristics, load signatures are extracted and processed within a hybrid machine-learning framework, are employed to cluster operating conditions into representative groups, and a regression estimator is applied to accurately infer the corresponding load coefficients. Based on these features, an optimal d-q axis current modulation scheme has been formulated to ensure distinct frequency deviations under islanded conditions. The effectiveness of the proposed methodology has been evaluated across a broad range of load scenarios, including those compliant with IEEE 1547 standards. Simulation results demonstrate that the method consistently detects islanding within 31 ms, while significantly reducing the non-detection zone compared to widely adopted Q-f droop and adaptive reactive power control techniques. Moreover, the proposed scheme alleviates transient voltage and frequency disturbances during grid disconnection, enabling smoother operational transitions. By integrating data-driven load assessment with optimal tuning of control parameters, the proposed framework enhances system reliability and detection responsiveness without requiring additional sensing hardware. Consequently, this approach serves as a promising solution for the robust and safe integration of inverter-based renewable energy resources in modern distribution networks.

Keywords

• Islanding Detection
• Inverter-Based Distributed Generation
• Machine-Learning
• Reactive Power Imbalance
• Load Characterization
• Gaussian Mixture Model (GMM)s New Roman