Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. Dispatchers are use the changed statuses of protection devices from the Supervisory Control and Data Acquisition (SCADA) system to identify the fault. To reduce the outage duration and promptly restore power services, fault section detection has to be done effectively and accurately with fault alarms.
In this thesis, artificial neural networks (ANN) and Grey Relational Analysis (GRA) are used to develop the restoration schemes for emergency operation in a power system including fault section detection (FSD), restoration strategy(RS), and voltage correction(VC). The optimal power flow (OPF) is responsible for verifying the proposed schemes by off-line analysis. With a IEEE 30-Bus power system, computer simulations were conducted to show the effectiveness of the proposed restoration schemes.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0123106-115927 |
| Date | 23 January 2006 |
| Creators | Chen, Chine-Ming |
| Contributors | Whei-Min Lin, Hong-Chan Chin, Sheng-Nian Yeh, Ming-Tong Tsay |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0123106-115927 |
| Rights | withheld, Copyright information available at source archive |
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