Fault location in distribution systems is a critical component of outage management and service restoration, which directly impacts feeder reliability and quality of the electricity supply. Improving fault location methods supports the Department of Energy (DOE) “Grid 2030” initiatives for grid modernization by improving reliability indices of the network. Improving customer average interruption duration index (CAIDI) and system average interruption duration index (SAIDI) are direct advantages of utilizing a suitable fault location method.
As distribution systems are gradually evolving into smart distribution systems, application of more accurate fault location methods based on gathered data from various Intelligent Electronic Devices (IEDs) installed along the feeders is quite feasible. How this may be done and what is the needed methodology to come to such solution is raised and then systematically answered. To reach this goal, the following tasks are carried out:
1) Existing fault location methods in distribution systems are surveyed and their strength and caveats are studied.
2) Characteristics of IEDs in distribution systems are studied and their impacts on fault location method selection and implementation are detailed.
3) A systematic approach for selecting optimal fault location method is proposed and implemented to pinpoint the most promising algorithms for a given set of application requirements.
4) An enhanced fault location method based on voltage sag data gathered from IEDs along the feeder is developed. The method solves the problem of multiple fault location estimations and produces more robust results.
5) An optimal IED placement approach for the enhanced fault location method is developed and practical considerations for its implementation are detailed.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2011-08-10001 |
| Date | 2011 August 1900 |
| Creators | Lotfifard, Saeed |
| Contributors | Kezunovic, Mladen |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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