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Title: Feeder reconfiguration on distribution network considering harmonics
Author: Peng, Zhengrui
ISNI:       0000 0004 6348 1809
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2017
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One of the most important measures that can be employed to enhance the supply reliability and quality of electrical energy in a distribution network is feeder reconfiguration. Many studies have been conducted in this area, but only a minor proportion have considered the concept of system harmonics in feeder reconfiguration. In recent years, an increasing number of invertor/converter-based renewable generators are being connected to the distribution network, and given that these generators are harmonic sources, it is important to consider the impacts of the system harmonics in the feeder reconfiguration. Load flow analysis is used to determine a suitable network structure for specific purposes in the feeder reconfiguration problem. In this thesis, a new load flow method is proposed based on the backward/forward sweep method. This method can analyse distribution network load flow under both fundamental and harmonic conditions with distributed generators. Following this, a hybrid optimization method is proposed based on the salient features of the ant colony system and particle swarm optimization. This hybrid method has a higher searching accuracy performance for feeder reconfiguration when compared, on test system, with the ant colony system and particle swarm optimization. Finally, a 118 mid-voltage level distribution system is used to investigate the impacts of renewable generators and system harmonics. The test results verify that system harmonics will have a significant influence on feeder reconfiguration and, consequently, cannot be ignored. Furthermore, other factors including the different capacities of renewable generators, fluctuations in load demands over 24 hours, and the variable output of the renewable generators in different seasons are also investigated in the feeder reconfiguration problem.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral