پیوندهای مفید
آمار
| تعداد نشریات | 33 |
| تعداد شمارهها | 878 |
| تعداد مقالات | 8,583 |
| تعداد مشاهده مقاله | 54,492,971 |
| تعداد دریافت فایل اصل مقاله | 10,119,323 |
Machine Learning Approaches for Islanding Detection in Inverter Based Distributed Generation Considering Load Characteristics | ||
| Computational Sciences and Engineering | ||
| دوره 5، شماره 2، آذر 2025، صفحه 295-309 اصل مقاله (1.05 M) | ||
| 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 | ||
| مراجع | ||
|
[1] Arif, A., Imran, K., Cui, Q., & Weng, Y. (2021). Islanding detection for inverter-based distributed generation using unsupervised anomaly detection. IEEE Access, 9, 90947–90963.
[2] Hussain, A., Mirza, S., & Kim, C.-H. (2023). Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks. Electric Power Systems Research, 224, 109807.
[3] Nsaif, Y. M., Lipu, M. H., Hussain, A., Ayob, A., & Yusof, Y. (2023). Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach. International Journal of Electrical Power & Energy Systems, 154, 109468.
[4] Hussain, A., Kim, C.-H., & Mehdi, A. (2021). A comprehensive review of intelligent islanding schemes and feature selection techniques for distributed generation system. IEEE Access, 9, 146603–146624.
[5] Kharusi, K. A., Haffar, A. E., & Mesbah, M. (2023). Adaptive machine-learning-based transmission line fault detection and classification connected to inverter-based generators. Energies, 16(15), 5775.
[6] Shadpey, S., Chegini, M., & Ameli, M.-T. (2023). A novel pattern recognition-based plan for islanding detection in distribution systems in the presence of synchronous generator-based distributed generation resources. Electrical Engineering, 105(3), 1609–1627.
[7] Kim, K., Park, Y., Lee, J., & Ju, S. (2024). Control strategy of energy storage system for islanding detection based on load analysis with machine learning. IEEE Transactions on Smart Grid, 15(5), 4719–4730.
[8] Chandrakar, R., Dubey, R. K., & Panigrahi, B. K. (2025). Deep learning-based islanding detection method for droop-controlled grid-forming inverters. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 6(2), 687–698.
[9] Ezzat, A., Elnaghi, B. E., & Abdelsalam, A. A. (2021). Microgrids islanding detection using Fourier transform and machine learning algorithm. Electric Power Systems Research, 196, 107224.
[10] Özcanlı, A. K., & Baysal, M. (2022). A novel multi-LSTM based deep learning method for islanding detection in the microgrid. Electric Power Systems Research, 202, 107574.
[11] Azizi, R., & Noroozian, R. (2021). Islanding detection in distributed energy resources based on gradient boosting algorithm. IET Renewable Power Generation, 15(2), 436–451.
[12] Allan, O. A., & Morsi, W. G. (2021). A new passive islanding detection approach using wavelets and deep learning for grid-connected photovoltaic systems. Electric Power Systems Research, 199, 107437.
[13] Uzair, M., Eskandari, M., Li, L., & Zhu, J. (2022). Machine learning based protection scheme for low voltage AC microgrids. Energies, 15(24), 9397.
[14] Bekhradian, R., & Sanaye-Pasand, M. (2022). A comprehensive survey on islanding detection methods of synchronous generator-based microgrids: Issues, solutions and future works. IEEE Access, 10, 76202–76219.
[15] Aminifar, F., Teimourzadeh, S., Shahsavari, A., Savaghebi, M., & Golsorkhi, M. S. (2021). Machine learning for protection of distribution networks and power electronics-interfaced systems. The Electricity Journal, 34(1), 106886.
[16] Islam, M. M., Nagrial, M., Rizk, J., & Hellany, A. (2021). General aspects, islanding detection, and energy management in microgrids: A review. Sustainability, 13(16), 9301.
[17] Zeineldin, H. H. (2009). A Q–f droop curve for facilitating islanding detection of inverter-based distributed generation. IEEE Transactions on Power Electronics, 24(3), 665–673.
[18] Morgan, M. Y., et al. (2025). Isolated microgrids dominant modes prediction based on machine learning. Protection and Control of Modern Power Systems, 10(4), 146–156.
[19] Li, C., Cao, C., Cao, Y., Kuang, Y., Zeng, L., & Fang, B. (2014). A review of islanding detection methods for microgrid. Renewable and Sustainable Energy Reviews, 35, 211–220.
[20] Khamis, A., et al. (2013). A review of islanding detection techniques for renewable distributed generation systems. Renewable and Sustainable Energy Reviews, 28, 483–493.
[21] Golpîra, H. (2025). A data-driven based approach for islanding detection in large-scale power systems. IEEE Transactions on Power Systems, 40(1), 272–285.
[22] Reddy, C. R., Choi, J. H., & Pangedaiah, B. (2024). Absolute positive sequence voltage difference based passive islanding detection in microgrids. IEEE Transactions on Industry Applications, 60(3), 4633–4641.
[23] Chhetija, D., Rather, Z. H., & Doolla, S. (2024). Fault location identification in power islands with inverter interfaced distributed generators. IEEE Transactions on Instrumentation and Measurement, 73, 1–10.
[24] Mumtaz, F., Imran, K., Rehman, H., & Bukhari, S. B. A. (2024). Novel protection method for AC microgrids with multiple distributed generations using unscented Kalman filter. Electric Power Systems Research, 230, 110227.
[25] Shahgholian, G., Moradian, M., & Fathollahi, A. (2025). Droop control strategy in inverter-based microgrids: A brief review on analysis and application in islanded mode of operation. IET Renewable Power Generation, 19(1), e13186.
[26] Seyedi, M., Taher, S. A., Ganji, B., & Guerrero, J. (2021). A hybrid islanding detection method based on the rates of changes in voltage and active power for the multi-inverter systems. IEEE Transactions on Smart Grid, 12(4), 2800–2811.
[27] Seyedi, M., Taher, S. A., Ganji, B., & Guerrero, J. M. (2019). A hybrid islanding detection technique for inverter-based distributed generator units. International Transactions on Electrical Energy Systems, 29(11), e12113.
[28] Seyedi, M., Taher, S. A., & Ganji, B. (2017). Islanding detection and frequency circuit measurement by power distribution relation depending on the angle. In Proceedings of the Conference on Electrical Power Distribution Networks (EPDC).
[29] Reynolds, D. A. (2009). Gaussian mixture models. In Encyclopedia of biometrics (pp. 659–663). Springer.
[30] Blei, D. M., & Jordan, M. I. (2006). Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1(1), 121–143. | ||
|
آمار تعداد مشاهده مقاله: 182 تعداد دریافت فایل اصل مقاله: 46 |
||