Power flow analytics for power distribution networks

Yu, Fangxintian (2025) Power flow analytics for power distribution networks. PhD thesis, University of Glasgow.

Abstract

Power flow analytics play a crucial role in the management and optimisation of power distribution networks, which are essential for ensuring the reliable and efficient delivery of electrical energy. This thesis explores the advanced methodologies and applications of power flow analytics within distribution networks, focusing on both the theoretical and practical aspects of analysing and improving network power transmitting performance.

The primary objective of this research is to enhance the understanding and applications of power flow analytics in the context of power networks at distribution levels. The research studies a range of analytical techniques to investigate power flow characteristics, including traditional methods and contemporary approaches. Key areas of focus include the development and application of advanced algorithms for power flow tracing, loss allocation, and the integration of new visualization techniques to aid in the interpretation of complex data. Simulation studies are conducted to evaluate the effectiveness of proposed power flow analytics methods. Significant findings include the loss allocation of complex power in distribution networks, and important applications. The benefits of integrating visualization tools are also highlighted, to enhance decision-making and operational management in power distribution networks. Finally, the application of power flow tracing to the wheeling charges calculation problem is investigated.

The conclusions drawn from this research underscore the importance of advanced power flow analytics in addressing the challenges faced by modern distribution networks. The study demonstrates that improved analytical methods can achieve accurate network performance and loss assessment and management, and effective planning and operation of power distribution systems.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Supervisor's Name: Yang, Dr. Jin
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-84924
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 26 Feb 2025 09:58
• Last Modified: 26 Feb 2025 10:05
• Thesis DOI: 10.5525/gla.thesis.84924
• URI: https://theses.gla.ac.uk/id/eprint/84924

Related URLs

• Conference proceeding