基于VMD分解和随机矩阵理论的异常用电状态检测

Detection of Abnormal Power Consumption State Based on VMD Decomposition and Random Matrix Theory

【目的】目前需要快速准确地判别用户异常用电行为。【方法】基于智能电表数据,提出了一种结合数据分解和随机矩阵理论的异常状态检测模型,实现了对用户用电异常行为的识别。通过变分模态分解算法(variational mode decomposition, VMD)剔除电力数据噪点,消除噪点数据影响。并将随机矩阵理论(random matrix theory, RMT)与自回归滑动平均模型(auto-regressive moving average model, ARMA)相结合,提高RMT对时间序列的适用性,实现了对用电异常状态的判定。【结果】以某地区的实际用电数据为例进行实验,验证了该方法针对数据样本较大且非高斯分布的情况具有便捷性和高效性,为用电异常行为的识别提供了新方向。

【Purposes】Users’abnormal power consumption behaviors need to be distinguished quickly and accurately.【Methods】An abnormal state detection model is proposed on the basis of smart meter data and data decomposition and random matrix theory,realizing the identification of users’abnormal power consumption behaviors.The variational mode decomposition(VMD)al-gorithm is used to eliminate the noise of power data and the influence of noise data.The Random Matrix Theory(RMT)is combined with the Auto-Regressive Moving Average Model(ARMA)to improve the applicability of RMT to time series and realize the judgment of abnormal state of electricity consumption.【Findings】Taking the actual power consumption data of a certain area as an example,the method conveniency and efficiency for the case of large data samples and non-Gaussian distribution have been verified,which provides a new direction for the identification of abnormal power consumption behavior.