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Machine Learning Approaches for Islanding Detection in Inverter Based Distributed Generation Considering Load Characteristics | ||
| Computational Sciences and Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 اسفند 1404 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22124/cse.2026.32608.1146 | ||
| نویسندگان | ||
| Fereshteh poorahangryan1؛ Masoumeh seyedi* 2 | ||
| 1Assistant Professor, Department of Electrical Engineering Ayandegan University, Tonekabon, Iran | ||
| 2Assistant Professor, Department of Electrical Engineering, Ayandegan University, Tonekabon, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Islanding Detection؛ Inverter-Based Distributed Generation؛ Machine-Learning؛ Reactive Power Imbalance؛ Load Characterization؛ Gaussian Mixture Model (GMM)s New Roman | ||
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آمار تعداد مشاهده مقاله: 7 |
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