基于随机矩阵理论和聚类算法的电能表运行状态评估方法

随着智能配电网络规模的扩大以及电网结构的复杂化，电力大数据呈指数级增长，电力设备的检、监测评估面临新的挑战。在大数据原理和数据挖掘分析的基础上，提出一种基于随机矩阵理论和聚类算法的电能表运行状态评估方法。首先，对电力大数据统一预处理，完成时间序列数据表征；然后，采用实时分离窗技术整合时序数据；其次，基于随机矩阵理论，对多维度电能表时间序列数据实时计算、分析统计量时序特征；进一步，采用改进的时间规整聚类算法计算时序数据相似度，从而对随机矩阵统计量聚类分级；最后，分析聚类结果，得到电能表运行状态评估等级和范围，完成电能表实时运行状态评估。实例分析和对比研究结果表明，与传统的主元分析评估方法相比，所提出的新型电能表运行状态评估方法具有良好的鲁棒性、可靠性和时效性，为电力电网检测技术应用研究提供了新思路。

Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm

With the expansion of intelligent distribution network and the increasing complexity of power grid structure, the amount of data in power system increases rapidly, and new challenges rise from the checking and monitoring evaluation of power equipment. Based on the principle of big data mining analysis, this paper proposes a method based on random matrix theory and clustering algorithm to evaluate the running state of electric energy meter. Firstly, time series data of various indicators are characterized and then integrated by real-time separation window technology. Based on the random matrix theory, the random matrix-based analysis model is constructed to calculate and analyze the characteristics with multi-dimensional statistical timing in real time. Further, an improved DTW (dynamic time warping) clustering algorithm is used to analysis the linear feature statistics of the output of the random matrix. Finally, according to the clustering result, the state of the electric energy meter is obtained and outputted as different classes. The experiments show that compared with the traditional Principal Component Analysis evaluation method, the proposed method has good robustness, reliability and timeliness, which provides a new idea for the application research of power grid detection technology.