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Study of Fault Detection and Restoration Strategy by Artificial Neural Networks

With the rapid growth of load demand, the distribution system is becoming more and more complicated, and the operational efficiency and service quality deteriorated. Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively with appeared fault alarms. The distribution system containing numerous protective facilities and switch equipment ranges over wide boundary. It becomes very complicated for dispatchers to obtain restoration plan for out-of-service areas. To cope with the problem, an effective tool is helpful for the restoration. This thesis proposes the use of Bi-directional associative memory networks (BAMN) to develop alarm processing. And use of Probabilistic Neural Network (PNN) to develop fault section detection, fault isolation, and restoration system. A distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed system.
The thesis proposes to use Bi-directional Associative Memory Network¡]BAMN¡^ to pre-process the signal gained from SCADA Interface, and transmit correct signal to Probabilistic Neural Network (PNN) for restoration plan . Computer simulation shows a simplified model to shorten the processing time in this study.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0630105-134833
Date30 June 2005
CreatorsWu, Yan-Ying
ContributorsTa-Peng Taso, Jen-Hao Teng, Whei-min Lin, Hong-Chan Chin
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0630105-134833
Rightscampus_withheld, Copyright information available at source archive

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