Adverse weather such as hurricanes can significantly affect the reliability of
composite power systems. Predicting the impact of hurricanes can help utilities for better
preparedness and make appropriate restoration arrangements. In this dissertation, the
impact of hurricanes on the reliability of composite power systems is investigated.
Firstly, the impact of adverse weather on the long-term reliability of composite
power systems is investigated by using Markov cut-set method. The Algorithms for the
implementation is developed. Here, two-state weather model is used. An algorithm for
sequential simulation is also developed to achieve the same goal. The results obtained by
using the two methods are compared. The comparison shows that the analytical method
can obtain comparable results and meantime it can be faster than the simulation method.
Secondly, the impact of hurricanes on the short-term reliability of composite
power systems is investigated. A fuzzy inference system is used to assess the failure rate
increment of system components. Here, different methods are used to build two types of
fuzzy inference systems. Considering the fact that hurricanes usually last only a few days, short-term minimal cut-set method is proposed to compute the time-specific
system and nodal reliability indices of composite power systems. The implementation
demonstrates that the proposed methodology is effective and efficient and is flexible in
its applications.
Thirdly, the impact of hurricanes on the short-term reliability of composite power
systems including common-cause failures is investigated. Here, two methods are
proposed to archive this goal. One of them uses a Bayesian network to alleviate the
dimensionality problem of conditional probability method. Another method extends
minimal cut-set method to accommodate common-cause failures. The implementation
results obtained by using the two methods are compared and their discrepancy is
analyzed.
Finally, the proposed methods in this dissertation are also applicable to other
applications in power systems.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-12-8716 |
| Date | 2010 December 1900 |
| Creators | Liu, Yong |
| Contributors | Singh, Chanan |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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