Premium
Model development for nonlinear dynamic energy conversion system: an advanced intelligent control paradigm for optimality and reliability
Author(s) -
Muhando Endusa Billy,
Senjyu Tomonobu,
Omine Eitaro,
Kinjo Hiroshi,
Funabashi Toshihisa
Publication year - 2008
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.20303
Subject(s) - control theory (sociology) , controller (irrigation) , drivetrain , reliability (semiconductor) , wind power , turbine , linear quadratic gaussian control , engineering , autoregressive model , nonlinear system , control engineering , rotor (electric) , electric power system , nonlinear autoregressive exogenous model , power (physics) , computer science , artificial neural network , control (management) , torque , mechanical engineering , physics , electrical engineering , quantum mechanics , artificial intelligence , agronomy , economics , econometrics , biology , thermodynamics , machine learning
Abstract Future prospects of the global wind industry are very promising: even on a conventional scenario the total wind power installed worldwide is projected to more than double from 74 GW by end of 2006 to 160 GW by 2012. The main challenge is wind stochasticity that impacts on both power quality and drive‐train fatigue from cyclic loading for a wind energy conversion system (WECS). To investigate amelioration of these problems, the approach in this study involves firstly, modeling: the wind speed is generated by an autoregressive moving average (ARMA) model, while the turbine, gearbox, and generator subsystems are represented with a spring‐mass‐damper mathematical equivalent. Secondly, an advanced intelligent control paradigm for optimality and reliability is formulated based on the models. The control strategy—involving design of a linear quadratic Gaussian (LQG) to damp these undesired oscillations—is applied to the detailed performability model. A pitch controller prevents rotor overspeed by ensuring the maximum power constraint. Computer simulations reveal that load reduction through ‘intelligent’ control systems becomes more attractive compared with designing mechanical systems to cope with large loads. © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.