摘要
为有效提升泛在电力物联网建设过程中的配电网运行安全感知能力,提出了一种基于多时间尺度状态估计的配电网实时态势预测方法,该方法可实现对配电网安全态势的快速、准确预测。首先,基于多元混合量测提出了多时间尺度递归动态状态估计方法,通过状态预测与伪量测递归变换改进了已有的量测线性等效变换方法,缩短了状态更新周期,在递归变换算法中添加了校正算法以消除伪量测波动误差,提高了状态估计算法的计算速度。然后基于状态估计得到历史估计状态,采用分区多元时间序列分析方法建立了实时状态预测模型,实现了配电网的实时安全性态势预测。最后在Matlab仿真平台中基于实际算例对所提方法进行了分析,结果验证了所提配电网实时态势预测方法的准确性和有效性。
In order to effectively improve the operation safety perception ability of distribution network in the process of ubiquitous power Internet of Things construction,a real-time situation prediction method of distribution network based on multi-time scale state estimation is proposed,which can realize fast and accurate prediction of distribution network security situation.First of all,the multi-timescale recursive dynamic state estimation is realized based on hybrid measurement including distribution PMU through recursive transformation between state projection and pseudo measurement,which realizes the fast equivalent transformation of measurement and shorten the state update period.A correction module is added in the recursive transformation algorithm to eliminate pseudo-measurement fluctuation error,improving the stability and calculation speed of the state estimation algorithm.Then,the real-time safety situation projection of distribution network is realized through partitioned multivariate time series analysis based on historic estimated states,which establishes a real-time state prediction model.Finally,practical examples are simulated and analyzed on MATLAB simulation platform.The simulation results validate the accuracy and effectiveness of the proposed real-time situation prediction method for distribution network.
作者
李延真
郭英雷
彭博
孙媛媛
李良子
LI Yanzhen;GUO Yinglei;PENG Bo;SUN Yuanyuan;LI Liangzi(State Grid Shandong Electric Power Company Qingdao Power Supply Company,Qingdao 266002,China;School of Electrical Engineering,Shandong University,Jinan 250061,China)
出处
《电力工程技术》
2020年第2期127-134,共8页
Electric Power Engineering Technology
基金
国家电网有限公司科技项目(SGYTHT/17-JS-201)。
关键词
配电网态势感知
泛在电力物联网
多时间尺度量测
状态估计
递归变换
distribution system situational awareness
ubiquitous power Internet of Things
multi-timescale measurement
state estimation
recursive estimation
