Abstract
When a single-phase ground fault occurs in the distribution network, it is generally allowed to operate with faults from 1 to 2 hours, which may lead to further development of the fault and even threaten the safe operation of the power system. Therefore, when a small current system has a ground fault, it must be quickly diagnosed to shorten the time for the distribution network operation and maintenance personnel to eliminate the fault, thereby improving the safety of the distribution network operation. It proposed to use PMU to measure the electrical quantity of each node of the distribution network synchronously, and upload the collected fault data set to the upper computer at high speed to train and test the deep belief network. By comparing the diagnosis results with the neural network trained without synchronous time-scale fault data set, it is proved that the deep belief network algorithm based on PMU optimized network parameter configuration is more accurate and efficient for single-phase ground fault identification of distribution network.
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