基于人工神经网络技术的电力设备状态实时监测研究  

Research on Real-Time Monitoring of Power Equipment Status Based on Artificial Neural Network Technology

作　　者：唐飞 TANG Fei(Nanjing Guodian Nanzi Weimei Automation Co.,Ltd.,Nanjing,Jiangsu 210031,China)

机构地区：[1]南京国电南自维美德自动化有限公司,江苏南京210031

出　　处：《自动化应用》2024年第8期118-120,共3页Automation Application

摘　　要：为提升电力系统的管理水平,及时掌握电力设备的运行情况,提出基于人工神经网络技术的电力设备状态实时监测方法。该方法以传感器采集的电力设备运行数据为基础,其数据模块通过变分模态分解方法完成设备运行状态数据中噪声数据的处理。将处理后的数据输出BP神经网络模型中,通过该模型学习和训练数据,输出电力设备异常状态检测结果,并利用递归图结构实现检测结果的可视化呈现。结果显示,该方法具备较好的应用效果,能可靠实现电力设备状态的实时监测,确定设备的异常运行结果,为电力系统管理提供可靠依据。To improve the management level of the power system and timely grasp the operation status of power equipment,a real-time monitoring method for power equipment status based on artificial neural network technology is proposed.This method is based on the operation data of power equipment collected by sensors.The data module processes the noise data in the equipment operation status data through the variational mode decomposition method.The processed data is output to a BP neural network model,which learns and trains data to output the abnormal state detection results of power equipment.The detection results are visualized using a recursive graph structure.The results show that this method has good application effects and can reliably achieve real-time monitoring of the status of power equipment,determine the abnormal operation results of the equipment,and provide a certain reliable basis for power system management.

关 键 词：人工神经网络技术 电力设备 状态实时监测 可视化呈现 

分 类 号：TM41[电气工程—电器]