摘要
为提高电厂设备运行质量,有效感知设备异常状态,针对基于孤立森林算法的电厂设备异常状态的自动化检测方法展开设计。通过传感器技术采集设备运行短期负荷数据后,利用孤立森林算法从正常样本中隔离出异常数据特征。通过深入分析隔离出的异常数据特征,识别与设备故障直接相关的信号,从而实现异常信号的自动化检测。实验结果表明,该方法能有效感知设备异常状态,可以满足电厂设备自动化运维需求。
In order to improve the operational quality of power plant equipment and effectively detect abnormal states of equipment,this study focuses on the design of an automated detection method for abnormal states of power plant equipment based on the isolation forest algorithm.This study collected short-term load data from devices using sensor technology and isolated abnormal data features from normal samples using the isolation forest algorithm.By analyzing the isolated abnormal data features in depth,identifying signals directly related to equipment failures,and achieving automated detection of abnormal signals.The experimental results show that this method can effectively perceive abnormal states of equipment and meet the requirements of automated operation and maintenance of power plant equipment.
作者
孙俊通
SUN Juntong(Qingdao Huafeng Weiye Electric Power Technology Engineering Co.,Ltd.,Qingdao,Shandong 266000,China)
出处
《自动化应用》
2025年第4期204-206,共3页
Automation Application
关键词
孤立森林算法
负荷电力数据
自动化检测
异常状态
电厂设备
isolated forest algorithm
load power data
automated detection
abnormal state
power plant equipment
