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.

引用

页数：7

共 10 条

[1] [Anonymous], IEEE SIGNAL PROCESS
[2] Gamm A.Z., 1976, Statistical methods for state estimation of electric power systems
[3] Haykin Simon., 2006, NEURAL NETWORKS COMP, Vsecond
[4] Huang N.E., 1998, EMPIRICAL MODE DECOM, V454
[5] Kurbatsky V., 2010, P 9 INT C ENV EL ENG, P381
[6] Kurbatsky V.G., 2009, P 4 INT C LIB MOD PO
[7] Kurbatsky V.G., 2009, P INT C POWERTECH 20
[8] Phadke AG, 2002, IEEE/PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXHIBITION 2002: ASIA PACIFIC, VOLS 1-3, CONFERENCE PROCEEDINGS, P476, DOI 10.1109/TDC.2002.1178427
[9] Tomin N.V., 2009, P INT YOUNG C EN IYC
[10] Voropai N.I., 2010, P C CONTR METH TECHN

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