摘要
电力系统频率态势在线预测有助于快速准确地制定扰动后的控制措施,降低事故影响。单一依靠物理或数据模型的频率态势在线预测方法在实际应用中存在计算速度与精度之间的矛盾。采用基于物理—数据融合建模思路,提出一种频率态势在线预测方法:将暂态频率影响因素划分为关键因素和非关键因素,对关键因素采用系统频率响应模型以保留电气信息间因果联系,对非关键因素采用基于极限学习机的误差校正模型以表征关联关系。该方法具有样本依赖性小、通信容错率高、计算效率受系统规模影响小的特点。通过标准测试系统仿真验证,表明所述方法能够快速、准确地预测系统受扰后的频率态势特征。
Post-disturbance frequency prediction of power system helps to determine reasonable subsequent control measures in time,through which impact of contingencies can be reduced.Separately implemented physical or statistical model methods for system frequency prediction are usually constrained with the conflict between speed and accuracy in practical application.A method for power system on-line frequency prediction based on physical and statistical combined modelling is proposed,which divides frequency prediction model with kernel part and non-kernel part.System frequency response model is applied in kernel part to reserve causal relationship among critical electrical information and error revising model constructed with extreme learning machine is applied in non-kernel part to incarnate connections among non-critical information.The implementation effect of the proposed method is limitedly affected by sample quantity,communication failure and system scale.Validations on standard testing systems demonstrate that the proposed method is able to predict post-frequency tendency correctly and promptly.
作者
王琦
李峰
汤奕
薛禹胜
WANG Qi;LI Feng;TANG Yi;XUE Yusheng(School of Electrical Engineering,Southeast University,Nanjing 210096,China;NARI Group Corporation（State Grid Electric Power Research Institute）,Nanjing 211106,China;State Key Laboratory of Smart Grid Protection and Control,Nanjing 211106,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2018年第19期1-9,共9页
Automation of Electric Power Systems
基金
国家重点研发计划资助项目(2017YFB0903000)
国家自然科学基金资助项目(51577030)
国家自然科学基金国际合作与交流项目(51561145011)~~
关键词
融合建模
频率态势预测
系统频率响应
极限学习机
combined modelling
frequency situation prediction
system frequency response
extreme learning machine
