In view of the incomplete data collection of the current low-voltage distribution network and the uncertainty of the to-pological relationship of the distribution platform,this study designs a household variation anomaly recognition system for the distribution platform.The anomaly recognition model of household variation relationship in the system is composed of BP neu-ral network and SOM neural network,which completes the clustering analysis and network mapping of data,and completes the anomaly recognition task based on the extracted feature information.A supercapacitor group is added to the topology identifica-tion module of the system,which can collect user information and report power outage accidents even when the system is pow-ered off.Experimental results show that the system has the highest recognition efficiency,the lowest recognition time is 2724 ms,and the highest anomaly recognition is 98.7％.
关键词
低压配电网拓扑/户变异常识别/BP神经网络/聚类分析/超级电容/停电事件
Key words
low-voltage distribution network topology/identification of household changes anomalies/BP neural network/clus-ter analysis/supercapacitor/power outage event
维普
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