Abstract
This thesis presents innovative methodologies for enhancing the operation of power systems through data analytics and numerical simulation within the context of Digital Twin technology. The research primarily addresses two critical advancements: the application of graph signal processing (GSP) to improve electric vehicle (EV) visibility at the regional grid level, and the exploration of frame transformation impacts on numerical integration in electromagnetic transient (EMT) simulations.This research contributes new data application on the regional grid level with graph signal processing and introduces the frame transformation impact on numerical integration of power system electromagnetic transient simulation, which all aim to achieve improved efficiency, reliability and sustainability for digital twin in power system.
Key contributions include the development of a non-intrusive monitoring approach leveraging GSP for EV disaggregation, which is essential for grid insight in an increasingly renewable energy-centric landscape. Additionally, the thesis investigates the suitability of numerical methods for power system EMT simulation, emphasizing the importance of accurate discretization frame selection for maintaining numerical performance.
The findings signify the potential of these approaches to offer more reliable and sustainable management of digital twins in power systems, providing a foundation for future exploration of smart grid technologies. The work concludes by encapsulating the implications of the research contributions for the broader field and suggesting avenues for future inquiry.
| Date of Award | 24 Apr 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Yunjie Gu (Supervisor) & Chenghong Gu (Supervisor) |