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Xiang Ma,Jianye Cui,Zhongming Xiang,Haoliang Du,Jianfeng Huang 한국정보과학회 2022 Journal of Computing Science and Engineering Vol.16 No.2
In the current distribution network information monitoring system, there are many false alarm information, which forms redundant interference to the fault alarm threshold, and it is difficult to ensure the alarm accuracy of the monitoring system. The distribution network intelligent information monitoring system and the alarm threshold adjustment method based on machine learning are designed, with the physical layer of the system designed to collect the operation status information of each line and equipment of the distribution network according to various sensors, and transfer it to the data layer. The data layer extracts, processes, and classifies the received information, stores it in the database, obtains the abnormal information in the information base, and adjusts the alarm threshold based on the fuzzy clustering method in machine learning, realizing intelligent monitoring of distribution network. The test results show that the detection performance of abnormal information is good, that the abnormal information in the data can be obtained accurately, and that the clustering of the target category of abnormal information can be completed according to the eigenvalue, and has a good threshold adaptive adjustment ability, to maximize the balance between human, machine, and power grid operation state in the process of distribution network monitoring information, ensure real-time and reliable monitoring and alarm results.
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