Fault identification and locating on PEA distribution system
Call Number: AIT Thesis no.ET-11-29 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. ET-11-29Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2011Description: 60 leaves : ill. + 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 2011 Summary: This thesis proposes an algorithm to identify and locate faults on the distribution system. The algorithm consists of two processes. Firstly, the impedance rule base is used to detect and identify the type of fault. Secondly, fault boundary factor (K) and a geographic information system (GIS) are applied to evaluate the possible fault location. The potential of the algorithm is promising for practical use. Average distance errors are less than 4.5%. The service restoration process will also be significantly faster. In addition, time spent by maintenance crews to locate the faults will be considerably decreased from 4-5 hours per case to approximate 1 hour per case in daytime and from 8-12 hours per case to approximate 2 hours per case in night time. Temporary faults can be also identified by the technique described in this thesis. The identification of particular areas with a high number of short duration faults are useful for maintenance workers.
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Asian Institute of Technology Library Archives | AIT Thesis no.ET-11-29 (Browse shelf(Opens below)) | Available | 30050120368047 |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy
Thesis (M.Eng.) - Asian Institute of Technology, 2011
This thesis proposes an algorithm to identify and locate faults on the distribution system. The algorithm consists of two processes. Firstly, the impedance rule base is used to detect and identify the type of fault. Secondly, fault boundary factor (K) and a geographic information system (GIS) are applied to evaluate the possible fault location. The potential of the algorithm is promising for practical use. Average distance errors are less than 4.5%. The service restoration process will also be significantly faster. In addition, time spent by maintenance crews to locate the faults will be considerably decreased from 4-5 hours per case to approximate 1 hour per case in daytime and from 8-12 hours per case to approximate 2 hours per case in night time. Temporary faults can be also identified by the technique described in this thesis. The identification of particular areas with a high number of short duration faults are useful for maintenance workers.
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