Research on Operation Stability Deterioration Trend Prediction Faced to Wind Power Group

Zhang Lei Jiang; Xiao Li Xu; Yun Bo Zuo; Zhi Jun Zhang

doi:10.4028/www.scientific.net/AMR.591-593.1973

Paper Titles

Dynamic Parameters Estimation Based on Set Inversion and Interval Analysis Algorithm
p.1954

Fault Detection of Electric Motors Application Using ML Estimation Method
p.1958

Simulation Analysis of Control Strategy for TLT Wheel Coordination Based on Constant Pressure Network
p.1962

Comparison of Two Two-Equation Turbulence Model Used for the Numerical Simulation of Underwater Ammunition Fuze Turbine Flow Field
p.1968

Research on Operation Stability Deterioration Trend Prediction Faced to Wind Power Group
p.1973

The Effects of Ply Angle of Laminated Composite Plates on Wind Induced Vibration
p.1978

Application of Support Vector Machine Regression in Failure Analysis of Fan Pump in Amphibious Assault Vehicles
p.1982

Fault Diagnosis of Induction Motor Based on Wavelet Packet Analysis and Negative Selection Algorithm
p.1986

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 591-593Research on Operation Stability Deterioration...

Research on Operation Stability Deterioration Trend Prediction Faced to Wind Power Group

Abstract:

A operation stability prediction model faced to wind power equipment group based on Internet of Things(IOT) is research. It’s beneficial to improve the operation stability of key equipment group and promote the emerging IOT technology application in equipment maintenance. The prediction model mainly including: based on IOT constructing the system of remote online sample acquisition and operation stability prediction; proposing operation trend prediction algorithm based on energy decoupling faced to wind power rotating machinery, realizing of operation trend feature extraction of equipment group; constructing operation stability trend prediction model faced to equipment group. Operation trend feature is used to predict fault trend of wind power in order to ensure safe and stable operation of wind power group.

You might also be interested in these eBooks

Manufacturing Engineering and Automation II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 591-593)

Pages:

1973-1977

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.591-593.1973

Citation:

Cite this paper

Online since:

November 2012

Authors:

Zhang Lei Jiang, Xiao Li Xu, Yun Bo Zuo, Zhi Jun Zhang

Keywords:

Energy Decoupling, Feature Extraction, Internet of Things (IOT), Trend Prediction, Wind Power Group

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] B.S. Stango. Proposed Embedded Security Framework for Internet of Things(IOT) [J] 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace &Electronic Systems Technology (Wireless VITAE),2011: p.5.

DOI: 10.1109/wirelessvitae.2011.5940923

Google Scholar

[2] D.R.Li, Z.F. Shao and X.M. Yang. Theory and Practice from Digital City to Smart City[J]. Geospatial Information, 2011,9(6):1-5.(In Chinese)

Google Scholar

[3] J.R. Mohanty, B.B. Verma and P.K. Ray. Prediction of Fatigue Crack Growth and Residual Life Using an Exponential Model: Part II (mode-I overload induced retardation). International Journal of Fatigue, 2009, 31(3): 425-432.

DOI: 10.1016/j.ijfatigue.2008.07.018

Google Scholar

[4] H.Q. Wang, K.Li, H.Sun andP.Chen. Feature Extraction Method Based on Pseudo Wigner Ville distribution for Rotational Machinery in Variable Operating Conditions, Chinese Jounal of Mechamical Engineering (English Edition), 2011, Vol. 24(4):661-668.

DOI: 10.3901/cjme.2011.04.661

Google Scholar

[5] X.L.Xu and H.J. Wang. Large rotating machinery running state trend forecasting [M]. Beijing: Science Press, 2011:274-306(In Chinese)

Google Scholar

[6] B.J.Xu, X.L.Xu andG.X.Wu. Fault diagnosis based on immune algorithm [J]. Journal of Beijing Information Science and Technology University, 2009,24(2):24-28

Google Scholar

[7] T.Chen. Study on Key Technologies of Nonlinear and Nonstationary Fault Prediction for Large Rotating Machinery[D].(Ph.D, Beijing Institute of Technology, china 2010).(In Chinese)

Google Scholar