Stochastic Distribution Controller for Wind Turbines with Doubly Fed Induction Generator
DOI:
https://doi.org/10.13052/dgaej2156-3306.3544Keywords:
PI controller, probability distribution, reactive power control, stochastic processes, wind turbine.Abstract
The major purpose of this work is to design the controllers for controlling the
variable speed, variable pitch wind turbine (WT) with doubly fed induction
generator (DFIG). Vector control strategy is adopted for controlling the
DFIG active and reactive power. Generator torque is control to provide the
regulated real power with minimum fluctuation. The fixed gain proportional-
integral (PI) controller designed to the converter of rotor side and grid
side has limited operating range and inherent overshoot. Gain scheduling
PI controller is designed to minimize the overshoot and fluctuation exists
in proportional-integral controller. Since DFIG based wind energy conver-
sion system (WECS) works in uncertain wind speed, stochastic distribution
control (SDC) method is proposed to control the probability distribution
function (PDF) of DFIG based WECS. It copes with nonlinearities in the
WECS and contiguous variations at operating point and provides satisfactory
performance for the whole operating region. It improves the performance
together with power quality of generated electric power thereby maximizing the lifespan of installation and ensures secure and acceptable operation of the
DFIG based WECS.
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References
J. Smith. (2005). Winds of change issues in utility wind integration –
Guest editorial. IEEE Power and Energy Magazine. 3, pp. 20–25.
E. Muhando, T. Senjyu, K. Uchida, H. Kinjo. & T. Funabashi. (2010).
Stochastic inequality constrained closed-loop model-based predictive
control of MW-class wind generating system in the electric power
supply. IET Renewable Power Generation. 4, pp. 23.
F.D. Bianchi, H. De Battista. & R.J. Mantz. (2006). Wind turbine control
systems: principles, modelling and gain scheduling design. Springer
Science & Business Media.
I. Munteanu, A.I. Bratcu, N.A. Cutululis. & E. Ceanga. (2008). Optimal
control of wind energy systems: towards a global approach. Springer
Science & Business Media.
J. Zhou, H. Yue, J. Zhang. & H. Wang. (2014). Iterative Learning Double
Closed-Loop Structure for Modeling and Controller Design of Output
StochAstic Distribution Control Systems. IEEE Transactions on Control
Systems Technology. 22, pp. 2261–2276.
B. Hamane, M. Benghanemm, A. Bouzid, A. Belabbes, M. Bouhamida.
& A. Draou. (2012). Control for Variable Speed Wind Turbine Driving
a Doubly Fed Induction Generator using Fuzzy-PI Control. Energy
Procedia. 18, pp. 476–485.
E. Bossanyi. (2000). The Design of closed loop controllers for wind
turbines. Wind Energy. 3, pp. 149–163.
Stochastic Distribution Controller for Wind Turbines 327
I. Munteanu, N. Cutululis, A. Bratcu. & E. Ceang ̆a. (2005) Optimiza-
tion of variable speed wind power systems based on a LQG approach.
Control Engineering Practice. 13, pp. 903–912.
F.D. Bianchi, H. De Battista. & R. J. Mantz. (2007). Wind turbine con-
trol systems: principles, modelling and gain scheduling design. Springer
Science & Business Media.
F. Bianchi, R. Mantz. & C. Christiansen. (2004). Control of variable-
speed wind turbines by LPV gain scheduling. Wind Energy. 7, pp. 1–8.
K. Bedoud, M. Ali-rachedi, T. Bahi. & R. Lakel. (2015). Adaptive
Fuzzy Gain Scheduling of PI Controller for Control of the Wind Energy
Conversion Systems. Energy Procedia. 74, pp. 211–225.
M. Soliman, O. Malik. & D. Westwick. (2011). Multiple Model Predic-
tive Control for Wind Turbines With Doubly Fed Induction Generators.
IEEE Transactions on Sustainable Energy. 2, pp. 215–225.
C. Sathish. & K.B. Mohanty. Performance analysis of doubly-fed induc-
tion generator in wind energy conversion system. M Tech Thesis,
National Institute of Technology.
A. Hansen, P. Sørensen, F. Iov. & F. Blaabjerg. (2004). Control of
Variable Speed Wind Turbines with Doubly-Fed Induction Generators.
Wind Engineering. 28, pp. 411–432.
T. Ghennam, E. Berkouk. & B. Francois. (2009). Modeling and control
of a Doubly Fed Induction Generator (DFIG) based Wind Conversion
System. 2009 International Conference on Power Engineering, Energy
and Electrical Drives.
B.K. Bose. (1986). Power electronics and AC drives.
B. Wu, Y. Lang, N. Zargari. & S. Kouro. (2011). Power conversion and
control of wind energy systems. John Wiley & Sons.
G. Abad, M. Rodriguez, G. Iwanski. & J. Poza. (2010). Direct
Power Control of Doubly-Fed-Induction-Generator-Based Wind Tur-
bines Under Unbalanced Grid Voltage. IEEE Transactions on Power
Electronics. 25, pp. 442–452.
S. Taher, M. Farshadnia. & M. Mozdianfard. (2013). Optimal gain
scheduling controller design of a pitch-controlled VS-WECS using DE
optimization algorithm. Applied Soft Computing. 13, pp. 2215–2223.
L. Guo. & H. Wang. (2010). Stochastic distribution control system
design: a convex optimization approach. Springer Science & Business
Media.
M. Brown. & C.J. Harris. (1994). Neurofuzzy adaptive modelling and
control. Prentice Hall.
Vijayalaxmi Munisamy et al.
H. Wang. (2002). Minimum entropy control of non-Gaussian dynamic
stochastic systems. IEEE Transactions on Automatic Control. 47,
pp. 398–403.
H. Wang. (2002). Minimum entropy control of non-Gaussian dynamic
stochastic systems. IEEE Transactions on Automatic Control. 47(2),
pp.398-403.
M. Ren, J. Zhang, Y. Tian. & G. Hou. (2014). A Neural Network
Controller for Variable-Speed Variable-Pitch Wind Energy Conversion
Systems Using Generalized Minimum Entropy Criterion. Mathematical
Problems in Engineering. 2014, pp. 1–9.
J.C. Principe. (2010). Information theoretic learning: Renyi’s entropy
and kernel perspectives. Springer Science & Business Media.
M. K ́arn ́y. (1996). Towards fully probabilistic control design. Automat-
ica. 32, pp. 1719–1722.