The increase of power demand by social production and life promotes the increase of distribution network scale, thus increasing the risk of faults. The generation of distribution network fault not only reduces the quality of power supply, but also reduces the security of the whole power grid; hence it is necessary to locate the fault quickly. This paper introduced two kinds of intelligent algorithms, ant colony algorithm (ACA) and quantum genetic algorithm (QGA), which were used to locate fault points with the help of fault information collected by Feeder Terminal Unit (FTU). Then, the simulation experiment was carried out in MATLAB taking the 33-node distribution network as the subject. The results showed that the accuracy of ACA and QGA for single-point and multi-point faults was higher than 95% when the fault information of FTU was not distorted; after the distortion of fault information, the accuracy of QGA for single-point and multi-point faults was basically the same, but the accuracy of ACA significantly reduced with the increase of distortion positions; in terms of detection time, whether the fault information was distorted or not, the time of QGA was less than that of ACA.
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