流计算与内存计算架构下的运营状态监测分析

为满足对电网实时运营状态分析过程中对用户实时用电量数据等大规模实时数据进行实时分析处理的需求，实现对电网运营决策提供快速准确的数据分析支持，提出一种流计算与内存计算相结合的大规模数据分析处理的系统架构。将经过时间窗划分的用户实时用电量数据进行离散傅里叶变换（DFT），实现对异常用电行为评价指标的构建；将基于抽样统计分析构造出的用户用电行为特征，采用K-Means聚类算法实现对用户用电行为类别的划分。从实际业务系统中抽取实验数据，验证了提出的异常用电行为和用户用电分析评价指标的准确性。同时，在实验数据集上与传统的数据处理策略进行对比，实验结果表明流计算与内存计算相结合的系统架构在大规模数据分析处理方面更具优势。

Monitoring and analysis of operation status under architecture of stream computing and memory computing

In real-time operation state analysis of power grid, in order to meet the requirements of real-time analysis and processing of large-scale real-time data, such as real-time electricity consumption data, and provide fast and accurate data analysis support for power grid operation decision, the system architecture for large-scale data analysis and processing based on stream computing and memory computing was proposed. The Discrete Fourier Transform (DFT) was used to construct abnormal electricity behavior evaluation index based on the real-time electricity consumption data of the users by time window. The K-Means clustering algorithm was used to classify the users' electricity behavior based on the characteristics of user electricity behavior constructed by sampling statistical analysis. The accuracy of the proposed evaluation indicators of abnormal behavior and user electricity behavior was verified by the experimental data extracted from the actual business system. At the same time, compared with the traditional data processing strategy, the system architecture combined with stream computing and memory computing has good performance in large-scale data analysis and processing.