电网大数据跨行业数据融合应用场景

Power Grid Big Data Cross-industry Data Fusion Application Scenarios

电网智能化的深入发展,使得跨行业数据,即异构多源数据(heterogeneous multi source data,HMSD)量激增,且数据的规模与特点符合大数据的特征。HMSD中不良数据的大量存在,能显著降低电网规划准确性和安全性。如何增强不良数据的分析能力,提高分析准确性,成为目前亟待解决的问题。文章利用Metropolis接受准则对大数据HMSD进行分析,从配电网负荷、电网运行状态、电能质量检测等应用场景出发,构建电网大数据跨行业数据融合应用场景评价模型,并进行MATLAB仿真分析。结果显示,所构建模型对于不良数据的综合辨识能力较佳,能够在1min内准确识别HMSD中的不良数据,准确率可达95%以上,所以该模型适用于电网大数据跨行业数据融合分析。

With the development of grid intelligence,the quantity of cross-industry data,namely heterogeneous multi-source data(HMSD),is increasing rapidly,and the scale and characteristics of data accord with the characteristics of big data.The existence of a large number of bad data in HMSD can significantly reduce the accuracy and security of power grid planning.How to enhance the analysis ability of bad data and improve the analysis accuracy has become an urgent problem to be solved.In this paper,Metropolis acceptance criterion is used to analyze the large data HMSD,and a cross-industry data fusion application scenario evaluation model is constructed based on the application scenarios of distribution network load,power network operation state and power quality detection,and carries on the MATLAB simulation analysis.The results show that the model constructed in this paper has better comprehensive identification ability for the bad data,and can accurately identify the bad data in the HMSD within 1 minute,the accuracy rate can reach more than 95%,therefore,this model is suitable for power grid big data cross-industry data fusion analysis.