Date of Award
5-2016
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Electrical Engineering
Committee Member
Elham Makram, Committee Chair
Committee Member
Elham Makram
Committee Member
Keith Corzine
Committee Member
Richard Groff
Abstract
Reliability analysis has been in place for decades, and its results are important for proper planning and operation of utility companies. Engineers must be able to quantify the current reliability of a system, as well as its potential improvement facing different modifications, in order to make informed planning decisions. Meanwhile, system operation has its performance measured through yearly reliability indices. The base of this method of analysis is the failure rate of the system components. In the traditional method, this probability of failure is determined by the components' manufacturer and is considered to be constant. However, it is reasonable to assume that the operation of the system has an effect on the likelihood of random failures to happen to the components. This study proposes a different modeling of failure rate, taking the system state variables into consideration. The probability of having system voltages or currents beyond the acceptable limits is added to the random probability of failure. With this new consideration, an IEEE test system has seven of its reliability indices quantified for comparison. The inclusion of the newly modeled failure rate lead to a worsening of 11.07% in the indices, on average. A second analysis is performed considering a third scenario, with PV and wind based micro sources present in the microgrid system, and an improvement of 0.71% on the indices is noticed, compared to the second scenario. Finally, the effects of storage systems in the microgrid are investigated through a fourth scenario, in which two 2MWh battery systems are introduced, and an improvement of 3.05% is noticed in the reliability indices.
Recommended Citation
Barreto de Medeiros, Rafael, "Assessment of Operating Condition Dependent Reliability Indices in Microgrids" (2016). All Theses. 2349.
https://open.clemson.edu/all_theses/2349