Novel Optimization-Based Algorithms for a Substation Voltage Controller Using Local PMU Measurements
Location
Hilton Waikoloa Village, Hawaii
Event Website
http://hicss.hawaii.edu/
Start Date
1-3-2018
End Date
1-6-2018
Description
This paper presents an improved version of a local voltage controller for a transmission substation. The controller uses available phasor measurement units (PMUs) at the substation, for optimal management of its local reactive (VAr) control resources, such as shunt reactive devices and transformer taps. Two optimization formulations with different objectives are introduced based on various operating criteria in electric utilities. The first approach aims to minimize the required reactive power injection such that it corrects the substation bus voltages to be within pre-specified limits so as to be close as possible to the optimal values. The second one minimizes the number of switching actions that are needed to correct the voltages to be within limits. Genetic algorithm (GA) is used for solving these discrete optimization problems. Performance of the proposed formulations is tested and analyzed through simulations for a typical substation in Southern California transmission network. Finally, the results from the two approaches are compared and discussed.
Novel Optimization-Based Algorithms for a Substation Voltage Controller Using Local PMU Measurements
Hilton Waikoloa Village, Hawaii
This paper presents an improved version of a local voltage controller for a transmission substation. The controller uses available phasor measurement units (PMUs) at the substation, for optimal management of its local reactive (VAr) control resources, such as shunt reactive devices and transformer taps. Two optimization formulations with different objectives are introduced based on various operating criteria in electric utilities. The first approach aims to minimize the required reactive power injection such that it corrects the substation bus voltages to be within pre-specified limits so as to be close as possible to the optimal values. The second one minimizes the number of switching actions that are needed to correct the voltages to be within limits. Genetic algorithm (GA) is used for solving these discrete optimization problems. Performance of the proposed formulations is tested and analyzed through simulations for a typical substation in Southern California transmission network. Finally, the results from the two approaches are compared and discussed.
https://aisel.aisnet.org/hicss-51/es/monitoring/3