Alternative Framework for Generator Coherency Analysis and Controlled-Islanding for Grids with High Penetration Levels of Inverter-Based Renewables
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Abstract
The increasing integration of inverter-based renewable energy sources has significantly altered power system dynamics and reduced inertia. Identifying coherent generators in such low-inertia systems remains a major challenge due to the dynamic influence of renewable variability. Previous research considered rotor angles and rotor speed separately. Also, the fast dynamics introduced by inverter-based renewables on power system variables make it pertinent to simultaneously consider rotor angles and rotor speed dynamics for coherency detection. Moreover, many authors have established coherency detection schemes but few have validated their methods with controlled islanding making their methods less practical for the modern grid with renewables. This paper proposes a unified coherency detection framework that combines rotor angles and rotor speeds within a dynamic state vector to capture both oscillatory and speed dynamics. An adaptive coherency threshold is computed from the geometric mean of the Euclidean distances between rotor state time series of distinct generators, allowing the threshold to adjust to changing system conditions. The framework is validated on the IEEE 30-bus system and applied to controlled islanding based on network topology and generation–load balance. The proposed framework achieved smooth dynamic responses following disturbances. These results confirm the method’s suitability for real-time application and its potential to improve resilience in modern power systems with high renewable integration.
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Samuel, I. A., Katende, J., Awosope, C. O. A., & Awelewa, A. A. (2017). Prediction of voltage collapse in electrical power system networks using a new voltage stability index. International Journal of Applied Engineering Research, 12(2), 190–199.
N.N., L., & V.V., V. (2018). Research into the loss of synchronism in power systems due to disturbances based on an analysis of electromechanical waves. Energy Systems Research, 1(2), 69–86. doi:10.25729/esr.2018.02.0008
Esmaeilian, A., & Kezunovic, M. (2017). Prevention of power grid blackouts using intentional islanding scheme. IEEE Transactions on Industry Applications, 53(1), 622–629. doi:10.1109/TIA.2016.2614772
Demetriou, P., Kyriacou, A., Kyriakides, E., & Panayiotou, C. (2019). Intentional Controlled Islanding of Power Systems Equipped With Battery Energy Storage Systems. 2019 IEEE Milan PowerTech, 1–6.
Fernández-Porras, P., Panteli, M., & Quirós-Tortós, J. (2018). Intentional controlled islanding: When to island for power system blackout prevention. IET Generation, Transmission and Distribution, 12(14), 3542–3549. doi:10.1049/iet-gtd.2017.1526
Samson, A. A., Popoola, O., & Mbey, V. (2021). An alternative framework for implementing generator coherency prediction and islanding detection scheme considering critical contingency in an interconnected power grid. International Journal of Applied Science and Engineering, 19(4), 1–12.
Zare, H., Yaghobi, H., & Alinejad-Beromi, Y. (2018). Adaptive concept of controlled islanding in power systems for wide-area out-of-step prediction of synchronous generators based on adaptive tripping index. IET Generation, Transmission and Distribution, 12(16), 3829–3836. doi:10.1049/iet-gtd.2018.0319
Lin, Z., Wen, F., Zhao, J., & Xue, Y. (2016). Controlled islanding schemes for interconnected power systems based on coherent generator group identification and wide-area measurements. Journal of Modern Power Systems and Clean Energy, 4(3), 440–453. doi:10.1007/s40565-016-0215-6
Babaei, M., & Abu-Siada, A. (2023). Intentional Controlled Islanding Strategy for Wind Power Plant Integrated Systems. Energies, 16(12). doi:10.3390/en16124572
Lin, Z., Wen, F., Ding, Y., Xue, Y., Liu, S., Zhao, Y., & Yi, S. (2018). WAMS-Based coherency detection for situational awareness in power systems with renewables. IEEE Transactions on Power Systems, 33(5), 5410–5426. doi:10.1109/TPWRS.2018.2820066
R. Monteiro, M., F. Alvarenga, G., R. Rodrigues, Y., Zambroni de Souza, A. C., L. Lopes, B. I., C. Passaro, M., & Abdelaziz, M. (2020). Network partitioning in coherent areas of static voltage stability applied to security region enhancement. International Journal of Electrical Power and Energy Systems, 117(August 2019), 105623. doi:10.1016/j.ijepes.2019.105623
Banna, H. U., Yu, Z., Shi, D., Wang, Z., Su, D., Xu, C., … Solanki, J. M. (2019). Online coherence identification using dynamic time warping for controlled islanding. Journal of Modern Power Systems and Clean Energy, 7(1), 38–54. doi:10.1007/s40565-018-0443-z
Demetriou, P., Hadjidemetriou, L., Kyriacou, A., Kyriakides, E., & Panayiotou, C. (2015). Real-time identification of coherent generator groups. 2015 IEEE Eindhoven PowerTech, PowerTech 2015. doi:10.1109/PTC.2015.7232619
Aghamohammadi, M. R., & Tabandeh, S. M. (2016). A new approach for online coherency identification in power systems based on correlation characteristics of generators rotor oscillations. International Journal of Electrical Power and Energy Systems, 83, 470–484. doi:10.1016/j.ijepes.2016.04.019
Khalil, A. M., & Iravani, R. (2016). A Dynamic Coherency Identification Method Based on Frequency Deviation Signals. IEEE Transactions on Power Systems, 31(3), 1779–1787. doi:10.1109/TPWRS.2015.2452212
Liu, S., Lin, Z., Zhao, Y., Liu, Y., Ding, Y., Zhang, B., … White, S. E. (2020). Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources. IEEE Transactions on Power Systems, 35(5), 3574–3587. doi:10.1109/TPWRS.2020.2971966
Wei, Y., Arunagirinathan, P., Arzani, A., & Kumar Venayagamoorthy, G. (2019). Situational awareness of coherency behavior of synchronous generators in a power system with utility-scale photovoltaics. Electric Power Systems Research, 172(July 2018), 38–49. doi:10.1016/j.epsr.2019.02.021
Yadav, R., Pradhan, A. K., & Kamwa, I. (2019). A Spectrum Similarity Approach for Identifying Coherency Change Patterns in Power System Due to Variability in Renewable Generation. IEEE Transactions on Power Systems, 34(5), 3769–3779. doi:10.1109/TPWRS.2019.2903848
Jafarzadeh, S., Genc, I., & Nehorai, A. (2021). Real-time transient stability prediction and coherency identification in power systems using Koopman mode analysis. Electric Power Systems Research, 201(March), 107565. doi:10.1016/j.epsr.2021.107565
Raak, F., Susuki, Y., & Hikihara, T. (2015). Multi-Way Partitioning of Power Networks via Koopman Mode Analysis. IFAC-PapersOnLine, 48(30), 421–426. doi:10.1016/j.ifacol.2015.12.415
Siddiqui, S. A., Verma, K., Niazi, K. R., & Fozdar, M. (2016). Real-time identification of generator coherent groups through synchrophasor measurements and ANN. 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 1–5. doi:10.1109/INDICON.2015.7443128
Gupta, A. K., Verma, K., & Niazi, K. R. (2018). Power system low frequency oscillations monitoring and Generator Coherency Determination in Real Time. IEEE Innovative Smart Grid Technologies - Asia (ISGTAsia).
Isnaadh, A., & Lukose, J. (2023). Generator coherency identification using a deep learning technique. In PROCEEDINGS OF 5TH INTERNATIONAL CONFERENCE ON SUSTAINABLE INNOVATION IN ENGINEERING AND TECHNOLOGY (SIET) 2023 (p. 020022). Kuala Lumpur, Malaysia: AIP Publishing. doi:10.1063/5.0229678
Quirós Tortos, J., Valverde, G., Ding, L., & Terzija, V. (2011). Optimal placement of Phasor Measurement Units to Improve Parallel Power System Restoration. IEEE PES Innovative Smart Grid Technologies Conference Europe. doi:10.1109/ISGTEurope.2011.6162687
Tortos, J. Q., & Terzija, V. (2012). Controlled islanding strategy considering power system restoration constraints. IEEE Power and Energy Society General Meeting, (September). doi:10.1109/PESGM.2012.6344599
Wang, X., DIng, L., Ma, Z., Azizipanah-Abarghooee, R., & Terzija, V. (2021). Perturbation-Based Sensitivity Analysis of Slow Coherency with Variable Power System Inertia. IEEE Transactions on Power Systems, 36(2), 1121–1129. doi:10.1109/TPWRS.2020.3020837
Song, H., Wu, J., & Wu, L. (2012). Controlled islanding based on slow-coherency and KWP theory. 2012 IEEE Innovative Smart Grid Technologies - Asia, ISGT Asia 2012, 1–6. doi:10.1109/ISGT-Asia.2012.6303096
Yang, B., Vittal, V., Heydt, G. T., & Sen, A. (2007). A novel slow coherency based graph theoretic islanding strategy. 2007 IEEE Power Engineering Society General Meeting, PES. doi:10.1109/PES.2007.386173
Chow, J. H., & Sanchez‐Gasca, J. J. (2019). Power System Coherency and Model Reduction. Power System Modeling, Computation, and Control. doi:10.1002/9781119546924.ch16
Stadler, J., Renner, H., & Köck, K. (2014). An inter-area oscillation based approach for coherency identification in power systems. Proceedings - 2014 Power Systems Computation Conference, PSCC 2014. doi:10.1109/PSCC.2014.7038314
Wilfert, H. H., Voigtländer, K., & Erlich, I. (2001). Dynamic coherency identification of generators using self-organising feature maps. Control Engineering Practice, 9(7), 769–775. doi:10.1016/S0967-0661(01)00028-4
Zhou, Z., Bai, X., Zhao, S. S., Li, Z., Xu, J., Li, X., & Zhao, W. (2008). A new islanding boundary searching approach based on slow coherency and graph theoretic. Proceedings - 4th International Conference on Natural Computation, ICNC 2008, 6(978), 438–442. doi:10.1109/ICNC.2008.523
Badeeb, O. M. A., & Hazza, G. A. W. (2004). Application of the slow coherency decomposition method to the Yemeni network. International Journal of Electrical Engineering and Education, 41(1), 56–63. doi:10.7227/IJEEE.41.1.5
Zhu, Y., Chen, Y., Li, L., Qi, D., Huang, J., & Song, X. (2025). A coherent generator group identification algorithm under extreme conditions. Global Energy Interconnection, 8(1), 92–105. doi:10.1016/j.gloei.2025.01.002
Song, H., Wu, J., & Wu, K. (2014). A wide-area measurement systems-based adaptive strategy for controlled islanding in bulk power systems. Energies, 7(4), 2631–2657. doi:10.3390/en7042631
Mariotto, L., Pinheiro, H., Cardoso, G., Morais, A. P., & Muraro, M. R. (2010). Power systems transient stability indices: An algorithm based on equivalent clusters of coherent generators. IET Generation, Transmission and Distribution, 4(11), 1223–1235. doi:10.1049/iet-gtd.2009.0647
Vahidnia, A., Ledwich, G., Palmer, E., & Ghosh, A. (2012). Generator coherency and area detection in large power systems. IET Generation, Transmission and Distribution, 6(9), 874–883. doi:10.1049/iet-gtd.2012.0091
Wang, M. H., & Chang, H. C. (1994). Novel Clustering Method for Coherency Identification Using an Artificial Neural Network. IEEE Transactions on Power Systems, 9(4), 2056–2062. doi:10.1109/59.331469
Davarikia, H., Znidi, F., Aghamohammadi, M. R., & Iqbal, K. (2016). Identification of coherent groups of generators based on synchronization coefficient. In IEEE Power and Energy Society General Meeting (Vol. 2016-Novem, pp. 6–10). Research gate. doi:10.1109/PESGM.2016.7741434
Khaitan, S. K., & McCalley, J. D. (2013). VANTAGE: A Lyapunov exponents based technique for identification of coherent groups of generators in power systems. Electric Power Systems Research, 105, 33–38. doi:10.1016/j.epsr.2013.07.004
Ourari, M. L., Dessaint, L. A., & Do, V. Q. (2003). Coherency Approach for Dynamic Equivalents of Large Power Systems. International Conference on Power Systems Transients – IPST 2003 in New Orleans, USA Coherency, 2(1), 1–6.
Ali, M., Mork, B. A., Bohmann, L. J., & Brown, L. E. (2013). Detection of coherent groups of generators and the need for system separation using synchrophasor data. Proceedings of the 2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013, (June), 7–12. doi:10.1109/PEOCO.2013.6564506
Lin, Z., Wen, F., Zhao, J., & Xue, Y. (2016). Controlled islanding schemes for interconnected power systems based on coherent generator group identification and wide-area measurements. Journal of Modern Power Systems and Clean Energy, 4(3), 440–453. doi:10.1007/s40565-016-0215-6
Demetriou, P., Quirós-Tortós, J., & Kyriakides, E. (2019). When to Island for Blackout Prevention. IEEE Systems Journal, 13(3), 3326–3336. doi:10.1109/JSYST.2018.2866937
Tang, F., Jia, J., Chen, L., Zhu, Z., Liu, J., Liao, Q., & Liu, D. (2016). Two-stage controlled islanding strategy based on Stoer-Wagner and improved Dinic algorithms. Journal of Modern Power Systems and Clean Energy, 4(3), 454–466. doi:10.1007/s40565-016-0206-7
Sun, K., Zheng, D. Z., & Lu, Q. (2005). A simulation study of OBDD-based proper splitting strategies for power systems under consideration of transient stability. IEEE Transactions on Power Systems, 20(1), 389–399. doi:10.1109/TPWRS.2004.841239
North American Electric Reliability Corporation (NERC). (2023). Reliability Guideline, Parameterization of the DER_A Model for Aggregate DER. North American Electric Reliability Corporation (NERC).
Subedi, S., Vasquez-Plaza, J. D., Andrade, F., Rekabdarkolaee, H. M., Fourney, R., Tonkoski, R., & Hansen, T. M. (2024). Aggregate data-driven dynamic modeling of active distribution networks with DERs for voltage stability studies. IET Renewable Power Generation, (July). doi:10.1049/rpg2.13063
Electric Power Research Institute (EPRI). (2019). The New Aggregated Distributed Energy Resources (DER_A) Model for Transmission Planning Studies: 2019 Update, 35.
IEEE 30-Bus System. (2025, October 21). Retrieved 21 October 2025, from https://electricgrids.engr.tamu.edu/electric-grid-test-cases/ieee-30-bus-system/