Operating Conditions Forecasting for Monitoring and Control of Electric Power Systems

被引:0
|
作者
Voropai, N. I. [1 ,2 ]
Glazunova, A. M.
Kurbatsky, V. G. [3 ,4 ]
Sidorov, D. N. [5 ,6 ]
Spiryaev, V. A.
Tomin, N. V. [3 ]
机构
[1] Russian Acad Sci, Energy Syst Inst, Siberian Energy Inst, Irkutsk, Russia
[2] Irkutsk Tech Univ, Irkutsk, Russia
[3] Russian Acad Sci, Energy Syst Inst, Irkutsk, Russia
[4] Int Res Inst Elect Engineers, New York, NY USA
[5] Trinity Coll Dublin, Dept Elect & Elect Engn, Dublin, Ireland
[6] CNRS, Compiegne, France
来源
2010 IEEE PES CONFERENCE ON INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT EUROPE) | 2010年
关键词
ANN; Electric power systems; forecasting Kalman filter; monitoring;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Two approaches are proposed for short-term forecast of the parameters of expected operating conditions. The Kalman filter based algorithms and the modern technologies of an artificial intelligence and nonlinear optimization algorithms are employed for dynamical state estimation. The new approach combining the artificial neural networks and the Hilbert-Huang transform is designed in order to increase the accuracy of operating conditions forecasting. Numerical experiments on real time series have demonstrated the improvement of the prediction.
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页数:7
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