能源互联网多能扰动识别的数据流处理模型

Data Stream Processing Model for Multi-energy Disturbance Identification of Energy Internet

电能、燃气、热能等多能源融合使能源在生产—调配—使用过程中极易发生多能扰动,传统的离线扰动识别方法很难满足能源互联网实时性的要求,采用数据流处理技术可对多能扰动信号进行在线识别。从能源互联网的能量—信息交互入手,对能源互联网多能扰动问题进行分析,为不同扰动信号建立数学模型。采用小波变换对扰动信号进行分解,并基于滑动窗口构建扰动信号的数据流处理模型。该模型首先构建滑动窗口概要数据结构,其次改进小波树更新算法以实现概要结构快速更新,优化扰动信号特征提取,最后采用决策树算法对信号特征进行分类。构建的数据流处理模型被应用到电能质量扰动和燃气质量扰动的识别中,验证该数据流处理模型的有效性。

Multi-energy disturbances might occur in the process of energy production,dispatch and use owing to the integration of electrical energy,natural gas,thermal energy,and so on.Traditional disturbance detection based on off-line data can hardly meet the real-time requirements of the energy internet.Data stream technology can be used to detect multi-energy disturbance signals on line.By proceeding from the interaction between energy and information,the energy internet multi-energy disturbance problem is analyzed,and mathematical models of different disturbance signals are developed.These disturbance signals are decomposed using wavelet transform and a signal data stream processing model of disturbance signals based on a sliding window with a synoptic data structure is built first.Secondly,for the purpose of rapid updating of the outline structure,the wavelet tree updating algorithm is improved.As a result the extraction of perturbation signal features is optimized and the decision tree algorithm is used to classify these signal features.Finally,the data stream processing model built in this article is applied to the identification of power quality disturbance and gas quality disturbance to verify the validity of the data stream model.