基于随机矩阵的海量智能电能表异常个体定位方法

智能电表是电力计量计费的核心装置,关系业主、电网等多方经济利益,具有数量庞大、运行环境复杂等特点。为有效精准发现海量智能电表的异常个体,提出了一种基于随机矩阵的海量智能电表异常个体定位方法。首先,提出了智能电表健康状态的多个参数表征方法,包括比差、角差、温度、湿度、震动等非电气量参数和一次电压、磁场等电气参数。其次,为了更加准确全面地对智能电表的状态进行评估,将智能电表的实时数据、仿真数据和历史运行数据等作为数据源,选取智能电表健康状态时的参数构建高维随机矩阵进行分析,实现了智能电表异常个体的定位。最后,采用南网新一代智能电表实际数据验证了文章所提方法的有效性,以期为我国智能电表在线运检提供借鉴。

Method for locating abnormal individuals in massive smart meters based on random matrix

Smart meters are the core device for electricity metering and billing, which is related to the economic interests of owners, power grids and other parties. It has the characteristics of a large number and complex operating environment. In order to effectively carry out online operation and inspection of massive smart meters, a random matrix-based method for locating abnormal individuals of massive smart meters is proposed. First, a number of parameter characterization methods for the health of smart meters are proposed, including non-electrical parameters such as ratio difference, angular difference, temperature, humidity, vibration, and electrical parameters such as primary voltage and magnetic field. Secondly, in order to evaluate the state of the smart meter more accurately and comprehensively, the data obtained from the test of the smart meter, simulation data and historical operating data are used as the data source, and the parameters of the health state of the smart meter are selected to construct a high-dimensional random matrix and analyze it, to realize the positioning of abnormal individuals of smart meters. Finally, the actual data of the new generation smart meter in China Southern Power Grid is used to verify the effectiveness of the method in this paper.