Abstract
As renewable energy sources are increasingly integrated and distributed in the power system, the number of converters in the grid is growing. This leads to more and more dynamics in the distribution networks and may result in more frequent disturbances such as voltage fluctuations, power quality problems, etc. To prevent possible threats to the security and stability of the network due to the above issues, Dynamic Security Assessment (DSA) is necessary for a distribution network to ensure its safe and reliable operation. In this work, an innovative approach to DSA using Digital Twin (DT) technology, with a primary focus on small signal stability (SSS) analysis using an eigenvalue method is proposed. The DT of the distribution network is first constructed by incorporating machine learning algorithms and updated in real-time using data from sensors and SCADA systems to accurately represent the behaviour of the physical system. The DT will be used to continuously monitor and assess the stability of the distribution network characterised by the presence of power electronic converters. The DSA then uses SSS to analyse network behaviour under different operating conditions and identify potential risks or disturbances. This can include assessing the impact of sudden changes in load or renewable energy output, detecting potential faults or equipment failures, and identifying potential instabilities in the power system. Numerical case studies are used to verify the viability of the proposed approach. As a result, by integrating a DT into the DSA process for converter-based distribution networks, operators can improve the accuracy and efficiency of their security measures, reduce the risk of outages, and increase the overall reliability of the system.
Zusammenfassung
Mit der zunehmenden Integration und Verteilung erneuerbarer Energiequellen im Stromnetz wächst die Anzahl der Stromrichter im Netz. Dies führt zu einer erhöhten Dynamik in den Verteilernetzen und kann zu häufigeren Störungen wie Spannungsschwankungen, Problemen mit der Netzqualität usw. führen. Um mögliche Bedrohungen für die Sicherheit und Stabilität des Netzwerks aufgrund dieser Probleme zu verhindern, ist eine dynamische Sicherheitsbewertung (DSB) für ein Verteilernetz notwendig, um einen sicheren und zuverlässigen Betrieb zu gewährleisten. In dieser Arbeit wird ein innovativer Ansatz für die DSB unter Verwendung des digitalen Zwillings (DZ)-Technologie vorgeschlagen, wobei der Schwerpunkt auf der Analyse der Kleinsignalstabilität (KSS) mit einer Eigenwertmethode liegt. Der DZ des Verteilernetzes wird zunächst durch Integration von maschinellem Lernen aufgebaut und in Echtzeit mit Daten von Sensoren und SCADA-Systemen aktualisiert, um das Verhalten des physischen Systems genau abzubilden. Der DZ wird verwendet, um die Stabilität des Verteilernetzes, das durch das Vorhandensein von Leistungselektronikstromrichtern charakterisiert wird, kontinuierlich zu überwachen und zu bewerten. Die DSB verwendet dann KSS, um das Netzwerkverhalten unter verschiedenen Betriebsbedingungen zu analysieren und potenzielle Risiken oder Störungen zu identifizieren. Dies kann die Bewertung der Auswirkungen plötzlicher Änderungen der Last oder der erneuerbaren Energieerzeugung, die Erkennung potenzieller Fehler oder Ausfälle von Ausrüstung sowie die Identifizierung möglicher Instabilitäten im Stromsystem umfassen. Numerische Fallstudien werden verwendet, um die Machbarkeit des vorgeschlagenen Ansatzes zu überprüfen. Durch die Integration eines DZ in den DSB-Prozess für stromrichterbasierte Verteilernetze können Betreiber die Genauigkeit und Effizienz ihrer Sicherheitsmaßnahmen verbessern, das Risiko von Ausfällen reduzieren und die Gesamtzuverlässigkeit des Systems erhöhen.
About the authors
Hui Cai (M’19) received her M.Sc. in Electrical Power and Control Engineering in 2019 at Technische Universität Ilmenau, Germany, where she joined the power systems group as a research fellow in the same year. Her main research interests focus on design, control, modelling and operation of converter dominated power systems and on the simulation of models in the real time simulator.
Dirk Westermann (M’94 – SM’05) received his Dipl.-Ing. and Dr.-Ing. at the University of Dortmund, Germany. In 1997 he joined ABB Ltd. in Zurich, Switzerland, where he held several positions in R&D and Technology Management. In 2005 he became director of the Institute of Electrical Power and Control Technologies. Since 2019 he is also responsible for the Centre of Energy Technology and the Institute for Energy, Drive and Environmental Systems Technology. His current research interests are power system operation and design with special attention to HVDC grids, system operation in the light of 100 % renewables, new architectures of power system control systems and assistance systems for better asset utilization.
-
Research ethics: Not applicable.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: The authors state no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
[1] E. Ciapessoni, D. Cirio, S. Massucco, A. Morini, A. Pitto, and F. Silvestro, “Risk-based dynamic security assessment for power system operation and operational planning,” Energies, vol. 10, no. 4, p. 475, 2017. https://doi.org/10.3390/en10040475.Search in Google Scholar
[2] F. Sanchez, F. Gonzalez-Longatt, and J. L. Rueda, “Security assessment of system frequency response,” in 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019, pp. 1–5.10.1109/PESGM40551.2019.8973555Search in Google Scholar
[3] C. Zhai, H. D. Nguyen, and X. Zong, “Dynamic security assessment of small-signal stability for power grids using windowed online Gaussian process,” IEEE Trans. Autom. Sci. Eng., vol. 20, pp. 1170–1179, 2022. https://doi.org/10.1109/tase.2022.3173368.Search in Google Scholar
[4] C. Zhai, H. D. Nguyen, and X. Zong, “Dynamic security assessment of small-signal stability for power grids using windowed online Gaussian process,” IEEE Trans. Autom. Sci. Eng., vol. 20, no. 2, pp. 1170–1179, 2023. https://doi.org/10.1109/TASE.2022.3173368.Search in Google Scholar
[5] J. L. Cremer and G. Strbac, “A machine-learning based probabilistic perspective on dynamic security assessment,” Int. J. Electr. Power Energy Syst., vol. 128, p. 106571, 2021. https://doi.org/10.1016/j.ijepes.2020.106571.Search in Google Scholar
[6] E. Ciapessoni, D. Cirio, G. Kjolle, S. Massucco, A. Pitto, and M. Sforna, “Probabilistic risk-based security assessment of power systems considering incumbent threats and uncertainties,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2890–2903, 2016. https://doi.org/10.1109/TSG.2016.2519239.Search in Google Scholar
[7] E. B. Espejo, F. R. S. Sevilla, and P. Korba, Eds. Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques, Amsterdam, Netherlands, Elsevier, 2023.Search in Google Scholar
[8] Q. Wu, T. J. Koo, and Y. Susuki, “Dynamic security analysis of power systems by a sampling-based algorithm,” ACM Trans. Cyber-Phys. Syst., vol. 2, no. 2, pp. 1–26, 2018. https://doi.org/10.1145/3208093.Search in Google Scholar
[9] K. P. Guddanti, B. Vyakaranam, K. Mahapatra, et al.., “Dynamic security analysis framework for future large grids with high renewable penetrations,” IEEE Access, vol. 11, pp. 8159–8171, 2023. https://doi.org/10.1109/ACCESS.2023.3238316.Search in Google Scholar
[10] S. A. Dorado-Rojas, M. de Castro Fernandes, and L. Vanfretti, “Synthetic training data generation for ML-based small-signal stability assessment,” in 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ, USA, 2020, pp. 1–7.10.1109/SmartGridComm47815.2020.9302991Search in Google Scholar
[11] S. V. Fernandes, D. V. João, B. B. Cardoso, M. A. I. Martins, and E. G. Carvalho, “Digital twin concept developing on an electrical distribution system – an application case,” Energies, vol. 15, no. 8, p. 2836, 2022. https://doi.org/10.3390/en15082836.Search in Google Scholar
[12] P. Palensky, M. Cvetkovic, D. Gusain, and A. Joseph, “Digital twins and their use in future power systems,” Digital Twin, vol. 1, no. 4, p. 4, 2022. https://doi.org/10.12688/digitaltwin.17435.2.Search in Google Scholar
[13] C. Shen, Q. Cao, M. Jia, Y. Chen, and S. Huang, “Concepts, characteristics and prospects of application of digital twin in power system,” Proc. CSEE, vol. 42, no. 2, pp. 487–498, 2022.Search in Google Scholar
[14] M. Zhou, J. Yan and D. Feng, “Digital twin framework and its application to power grid online analysis,” CSEE JPES, vol. 5, no. 3, pp. 391–398, 2019.Search in Google Scholar
[15] N. Tzanis, N. Andriopoulos, A. Magklaras, E. Mylonas, M. Birbas, and A. Birbas, “A hybrid cyber physical digital twin approach for smart grid fault prediction,” in 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 2020, pp. 393–397.10.1109/ICPS48405.2020.9274723Search in Google Scholar
[16] F. Arrano-Vargas and G. Konstantinou, “Modular design and real-time simulators toward power system digital twins implementation,” IEEE Trans. Ind. Inf., vol. 19, no. 1, pp. 52–61, 2023. https://doi.org/10.1109/TII.2022.3178713.Search in Google Scholar
[17] L. Wu, Z. Chen, C. Long, et al.., “Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm,” Appl. Energy, vol. 232, pp. 36–53, 2018. https://doi.org/10.1016/j.apenergy.2018.09.161.Search in Google Scholar
[18] Conseil international des grands réseaux électriques. Comité d’études C6, Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources, Paris, France, Cigré, 2014.Search in Google Scholar
[19] K. Madsen, H. Nielsen, and O. Tingleff, Methods for Non-Linear Least Squares Problems, 2nd ed. Denmark, Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2004, p. 60.Search in Google Scholar
[20] L. Babani, S. Jadhav, and B. Chaudhari, “Scaled conjugate gradient based adaptive ANN control for SVM-DTC induction motor drive,” in Artificial Intelligence Applications and Innovations, Cham, 2016, pp. 384–395.10.1007/978-3-319-44944-9_33Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
