基于改进K-means的电能表时钟异常状态智能检测方法

Intelligent Detection Method for Abnormal Clock State of Electric Energy Meter Based on Improved K-means

为了最大限度降低因时钟状态异常给电能表性能带来的影响,提出基于K-means的电能表时钟异常状态检测方法。采用K-means聚类算法对原始的时钟运行数据进行预处理,利用阈值参量将时钟信号划分为噪声信号和正常信号两类,再计算待检测时钟信号中噪声信号的含量,并通过比较计算结果与时钟允许噪声含量之间的关系,实现异常状态检测。测试结果表明,设计方法实现了对时钟不同程度异常状态的准确检测,可靠性较高。

In order to minimize the impact of abnormal clock state on the performance of electric energy meters,this paper proposes a detection method of abnormal clock state of electric energy meters based on K-means.K-means clustering algorithm is used to preprocess the original clock operation data,and the clock signal is divided into noise signal and normal signal by threshold parameter.Calculate the content of noise signal in the clock signal to be detected and realize abnormal state detection by comparing the relationship between the calculation result and the allowable noise content of the clock.The test results show that the design method realizes the accurate detection of different degrees of abnormal state of the clock and has high reliability.