Stochastic Distribution Controller for Wind Turbines with Doubly Fed Induction Generator

Authors

  • Vijayalaxmi Munisamy Department of Electrical and Electronics Engineering, College of Engineering, Guindy, Anna University, Chennai, India
  • Nayagam Shanmuga Vadivoo Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India
  • Vaithilingam Devasena Department of Electrical and Electronics Engineering, College of Engineering, Guindy, Anna University, Chennai, India

DOI:

https://doi.org/10.13052/dgaej2156-3306.3544

Keywords:

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.

Downloads

Download data is not yet available.

Author Biographies

Vijayalaxmi Munisamy, Department of Electrical and Electronics Engineering, College of Engineering, Guindy, Anna University, Chennai, India

Vijayalaxmi Munisamy received B.E Degree in EEE from RVS College
of Engineering, Dindigul, affliated to Madurai Kamaraj University, Tamil-
nadu in 2000. M.Tech Degree in Process Control and Instrumentation from
National Institute of Technology, Trichy, affiliated to Bharathidasan Uni-
versity, Tamilnadu in 2002. She is working as Assistant Professor in the
Department of EEE, College of Engineering, Guindy, Anna University,
Chennai. Completed Ph.D. in Control Systems from Faculty of Electrical and
Electronics Engineering, Thiagarajar College of Engineering, Madurai, Anna
University in 2018. Her research interest includes control systems, Renew-
able Energy, Electrical Machines and Measurement and Instrumentation.

Nayagam Shanmuga Vadivoo, Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India

Nayagam Shanmuga Vadivoo received B.E Degree in EEE from Thia-
garajar College of Engineering, Madurai, Tamilnadu, in 1993. M.E Degree
in Power Systems from Thiagarajar College of Engineering, Madurai,
Tamilnadu in 1994. Completed Ph.D in the Faculty of Electrical and Electron-
ics Engineering from Madurai Kamaraj University, Tamilnadu in 2009. She is
working as Professor in the Department of EEE, Thiagarajar College of Engi-
neering, Madurai, Tamilnadu. Her research interest includes Power systems,
Renewable Energy, Distributed Generation and soft computing techniques.

Vaithilingam Devasena, Department of Electrical and Electronics Engineering, College of Engineering, Guindy, Anna University, Chennai, India

Vaithilingam Devasena received B.E degree in Electronics and Instru-
mentation Engineering from Arunai Engineering College, Thiruvannamalai,
Tamilnadu in 2012. M.E degree in Control and Instrumentation from College
of Engineering, Guindy, Anna Univesity, Chennai in 2016. Her research
interest includes Control systems, Measurements and Instrumentation and
Renewable Energy.

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.

Published

2021-04-28

How to Cite

Munisamy, V. ., Vadivoo, N. S. ., & Devasena, V. . (2021). Stochastic Distribution Controller for Wind Turbines with Doubly Fed Induction Generator. Distributed Generation &Amp; Alternative Energy Journal, 35(4), 307–330. https://doi.org/10.13052/dgaej2156-3306.3544

Issue

Section

Articles