Enhancing dynamic security assessment in converter dominated distribution networks through digital twin-based small signal stability analysis

Abstract

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.