基于知识图谱的电力变压器绝缘故障监测方法

Power Transformer Insulation Fault Monitoring Method Based on Knowledge Graph

通过传感器采集电力变压器数据,只能获取当前运行数据,导致变压器绝缘故障监测结果与实际情况不一致,为此提出基于知识图谱的电力变压器绝缘故障监测方法。采用RBF神经网络建立了变压器绝缘故障监测模型;依据知识图谱三元组成和生命周期,设计数据抽取步骤,抽取变压器历史数据,构建电力变压器知识图谱;设置变压器温度和功率值,设计故障监测步骤,实现变压器绝缘故障监测。实验结果表明:本文方法在案例1和案例2中,监测变压器绝缘故障产生原因与案例设置原因一致,监测效果较好。

At present,only the current operation data can be obtained by collecting power transformer data through sensors,resulting in the inconsistency between transformer insulation fault monitoring results and the actual situation.Therefore,a power transformer insulation fault monitoring method based on knowledge map is proposed.RBF neural network is used to establish the transformer insulation fault monitoring model.According to the ternary composition and life cycle of knowledge map,we design data extraction steps to extract transformer historical data,and construct power transformer knowledge map.We then set the transformer temperature and power value,design the fault monitoring steps,and realize the transformer insulation fault monitoring.The experimental results show that in Case 1 and Case 2,the cause of transformer insulation fault is consistent with the cause of case setting,and the monitoring effect is good.