Open Access Dissertation
Voltage regulators; Electric power distribution;
Energy demand has rapidly increased since the manufacturing revolution in the 19th century. One of the higher energy demands is electricity. The great majority of devices in the manufacturing field run on electricity. The vertically integrated grid paradigm has to be changed to supply the increase in the electrical demand residentially and commercially. Distributed generators (DG) such as Photovoltic (PV) is used to supply the increase in the electrical demand. Photovoltaic (PV) is one of the fast growing distributed generators (DG) as a renewable energy source. However, installing many PV systems to the distribution system can cause power quality problems such as over voltage. This would be more concern in an unbalanced electrical distribution network where nowadays most of the PV systems are connected. PV system should coordinate with other DGs and already existing voltage regulators such as on load tap changer (OLTC) on voltage regulation so that they can support the electrical grid without adding voltage problems. This dissertation focuses on voltage regulation of unbalanced distribution system through the utilization of PV reactive power feature by minimizing the system losses using genetic algorithm. The proposed method provides a single phase controlled PV system that regulates each phase voltage individually and focuses in maintaining the voltage for each phase within a certain limit. In addition, this study proposes a single-phase OLTC control by changing the tap position individually using loss minimization. The proposed algorithm is implemented in Matlab and Simulink. Results show that the PV reactive power can be utilized to control the system voltage as well as to minimize the traditional voltage regulator operations.
Year of Submission
Doctor of Industrial Technology
Department of Technology
Hong Nie, Committee Chair
Sadik Kucuksari, Co-Chair
1 PDF file (xi, 84 pages)
©2018 Islam Ali
Ali, Islam, "Voltage regulation of unbalanced distribution network with distributed generators through genetic algorithm" (2018). Dissertations and Theses @ UNI. 678.