University of Guilan Computational Sciences and Engineering 2783-2503 Articles in Press 2026 02 21 Machine Learning Approaches for Islanding Detection in Inverter Based Distributed Generation Considering Load Characteristics 9461 10.22124/cse.2026.32608.1146 EN Fereshteh Poorahangryan Assistant Professor, Department of Electrical Engineering Ayandegan University, Tonekabon, Iran Masoumeh Seyedi Assistant Professor, Department of Electrical Engineering, Ayandegan University, Tonekabon, Iran Journal Article 2025 12 22 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.