基于光纤传感器的电气设备状态检测方法  

State Detection Method of Electrical Equipment Based on Optical Fiber Sensor

作　　者：施力仁[1] SHI Liren(Information Technology Department,Zhongshan Technical Secondary School,Zhongshan 528400,China)

机构地区：[1]中山市中等专业学校,信息技术部,广东中山528400

出　　处：《微型电脑应用》2022年第3期148-150,168,共4页Microcomputer Applications

摘　　要：针对当前方法存在的电气设备状态检测正确率低,误检率、漏检率高居不下的难题,为了改善电气设备状态检测结果,设计基于光纤传感器的电气设备状态检测方法。分析电气设备状态检测的研究现状,找到各种电气设备状态检测方法的局限性;通过光纤传感器采集电气设备状态信号,对电气设备状态信号进行预处理,并从电气设备状态信号中提取特征;采用RBF神经网络根据特征拟合电气设备状态变化特点,建立电气设备状态检测模型,并与其他电气设备状态检测方法进行仿真对比测试实验。实验结果表明,提出的方法的电气设备状态检测正确率超过90%,电气设备状态的误检率、漏检率均低于5%,检测效果明显优于对比方法,可以应用于实际的电气设备安全维护中。Aiming at the problems of low accuracy rate, high false detection rate and missing detection rate in the current method of electrical equipment state detection, in order to improve the results of electrical equipment state detection, a state detection method of electrical equipment based on optical fiber sensor is designed. The research status of electrical equipment state detection is analyzed, and the limitations of various electrical equipment state detection methods are found. The electrical equipment status signal is collected by optical fiber sensor, the electrical equipment status signal is preprocessed, and the characteristics are extracted from the electrical equipment status signal. RBF neural network is used to fit the characteristics of electrical equipment state change according to the characteristics, and the electrical equipment state detection method is established. The results show that the accuracy rate of electrical equipment state detection is more than 90%, and the false detection rate and missing detection rate of electrical equipment state are all less than 5%. The detection effect of electrical equipment state detection is obviously better than that of other current methods, and it can be applied to the actual electrical equipment safety maintenance.

关 键 词：电气设备 状态信号 RBF神经网络 信号去噪 检测效率 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]