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基于时间序列的主网变电继电保护装置自动化监测系统

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变电继电保护装置运行状态,直接影响主网的运行安全,为实时掌握变电继电保护装置运行情况,设计基于时间序列的主网变电继电保护装置自动化监测系统.系统前端数据采集层通过无线传感节点采集变电继电保护装置运行状态时序数据,通过网络接口传送至数据处理及运算层中,该层利用数据汇集节点聚合时序数据后,基于时间序列处理异常数据以及缺失数据修复;上层业务应用层以存储的时序数据为依据,基于时谱神经网络的时间序列预测模型,预测主网变电继电保护装置运行状态,并进行状态自动化预警.测试结果显示:该系统具备较好的数据处理效果,能够删除数据中的异常数据,并实现缺失数据修复;判定系数结果均在 0.955 以上,预测效果较好;能够获取该装置的异常状态结果,并且针对预测结果进行预警.
Automatic monitoring system for main grid substation relay protection devices based on time series
The operation status of substation relay protection devices directly affects the safety of the main network.Therefore,in order to real-time grasp the operation status of substation relay protection devices,a time series based automation monitoring system for the main network substation relay protection devices is designed.The data acquisition lay in that front end of the system collects the time sequence data of the operation state of the substation relay protection device through the wireless sensor node,and transmits the time sequence data to the data processing and operation layer through the network interface;aft the time sequence data are aggregated by the data collection node,the layer processes abnormal data and repairs missing data based on the time sequence;Based on the stored time series data and the time series prediction model of time spectrum neural network,the upper business application layer pre-dicts the operation state of substation relay protection devices in the main network,and carries out automatic early warning of the state.The test results show that the system has good data processing performance,can delete abnormal data in the data,and achieve true data repair;The judgment coefficient results are all above 0.955,indicating good predictive performance;Able to obtain abnor-mal status results of the device and provide warnings based on predicted results.

time seriesmain network substation relayprotective devicesautomated monitoringtime series data processingautomated early warning

曾繁玲

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罗定职业技术学院机电工程系,广东 罗定 527200

时间序列 主网变电继电 保护装置 自动化监测 时序数据处理 自动化预警

2024

自动化与仪器仪表
重庆工业自动化仪表研究所,重庆市自动化与仪器仪表学会

自动化与仪器仪表

CSTPCD
影响因子:0.327
ISSN:1001-9227
年,卷(期):2024.(11)