| Summary: | 碩士 === 國立中山大學 === 電機工程學系研究所 === 96 === With the application of high tech equipments in the industrial, commercial, and residential areas, the enhancement of power quality has become a very critical issue for power utilities. Distribution automation and customer automation will be the most important functions to be implemented. Distribution automation consists of control center, communication system, remote terminal units, and feeder terminal units. The currents, voltages, phase angles, and switch status will be collected by the real time SCADA system. Based on the switch status and connectivity analysis, the distribution network configuration can be identified by executing the topology process. The distribution system planning and optimal operation strategy can be achieved by various application software functions.
A multiagent-based distribution automation system is developed for fault detection, isolation, and restoration (FDIR) of distribution systems with JADE platform in this thesis. Remote terminal unit (RTU) agents, main transformer (MTR) agents, feeder circuit breaker (FCB) agents, and feeder terminal unit (FTU) agents of the multiagent system (MAS) are proposed to derive a proper restoration plan after a faulted location has been identified and isolated. To ensure the fault restoration plan can comply with the operation regulation, heuristic rules based on the standard operation procedures of Taipower distribution system are included in the best first search of the MAS. For the fault contingency during summer peak season, the load shedding may be executed and the MAS are designed to restore service to as many key customers and loads as possible. The priority indices of each feeder and service zone are determined according to the key customers within the service territory.
A Taipower distribution system with 43 feeders is selected for computer simulation to demonstrate the effectiveness of the proposed methodology in this thesis. It is found that the service restoration of distribution system can be obtained very efficiently by applying the proposed multiagent-based MAS.
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