鑑於用戶對於供電品質之要求與日俱增，故當電力系統發生故障時，如何迅速找出故障區域，俾於進行維修復電，確已成為重要課題。基於此，本文即提出細菌趨化尋優演算法來求解電力系統故障診斷的問題，此方法乃模擬大腸桿菌於人體腸道內進行趨化作用(Chemotaxis)以獲取養分之行為，因此可應用於尋優最佳化問題之求解。其中本文經由實際系統進行測試，以驗證細菌趨化尋優算法於配電系統故障診斷之效能，並將其與其它方法測試比較，而由結果顯示，此細菌趨化尋優演算法確具較佳運算效能，且能以較少之疊代次數判定故障區段，應有助於工程人員即時預警及施行相關維修之需。

The problem of fault section estimation is very important in power systems because effective fault section estimation will help facilitate the power restoration. Therefore, in this thesis, a Bacterial Foraging Algorithm (BFA) is proposed to solve the fault section estimation problem. The theme of this method is to simulate Escherichia coli such that the nutrients in the intestine can be better searched, with such a concept it is further employed to solve the fault section estimation problems. In order to validate the effectiveness of this approach, the method has been tested through different test scenarios with comparison to other methods. From the test results, they revealed the satisfactory computation performance of justifying the section of faults in a distribution system, thereby providing a useful reference as maintenance forewarning as well.

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