This document discusses enhancing the pitch control characteristics of a DFIG (doubly fed induction generator) based wind turbine using ANFIS (adaptive neuro fuzzy inference system). It compares the pitch control, mechanical torque characteristics, and rotor speed with and without ANFIS control. When using ANFIS, the characteristics are improved both with speed error and the difference of speed and power errors as inputs, but they are enhanced most with the latter. The simulation is conducted in MATLAB/Simulink and results show ANFIS control improves the pitch control characteristics of a DFIG based wind turbine.

1. International Journal of Current Research In Science, Engineering and Technology (IJCRSET)
Volume 1, Issue 2, June 2016, PP 07-12
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Enhanced pitch control characteristics of a DFIG based wind
turbine using ANFIS
1
Y.Venkata Lakshmi, 2
Muralikrishna.N
1
(.G. Scholar, Dept.of EEE, Srivani School of Engineering, chevuturu, A.P., India)
2
(Hod & Asst.Prof., Dept.of EEE, Srivani School of Engineering, chevuturu, A.P., India)
Abstract : In this paper the pitch control characteristics of a dfig based wind turbine are computed.The
comparison is made without ANFIS, with ANFIS with speed error as input and with ANFIS with speed and
power error difference as input.The mechanical torque characteristics are also plotted. The entire simulation in
conducted on mat lab/simulink environment.
Keywords :DFIG, ANFIS, pitch control, mechanical torque
I. INTRODUCTION
Today extracting power from wind has become sophisticated issue due to the increased usage of electrical
power and limited availability of conventional sources [1].controlling the extracted power is one of the major
tasks. By enhancement of pitch the mechanical torque is improved which improves mechanical power. In the
previous days the individual pitch control was used for better operation. Now days, the IPC is replaced by fuzzy
logic control which gives better results compared to IPC.The latest technique in fuzzy is adaptive neuro fuzzy
inference system which is easy to form rules. By adjusting the pitch we can obtain required power output [2].the
overall dfig based wind scheme is shown in Fig.1.
II.WIND ENERGY CONVERSION SYSTEM
The overall scheme of wind system shown in Fig.1.consists of wind turbine, drive train, dfig, pwm inverter,
rectifier, control scheme etc. The kinetic energy is converted into mechanical energy and mechanical energy to
Fig.1.overall scheme of DFIG based wind turbine
DFIG
PWM
inverter
Rectifier
3-Ø GRID
CONTROL
Gear
Pitch control
ANFIS

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electrical energy by using an induction generator. The equations of mechanical power and torque and tip-speed
ratio are given by the equations (1)-(3) [3]:
(1)
(2)
(3)
Where Ὡ corresponds to turbine angular speed, R is the blade length is the wind speed and Cp is the power
coefficient.
III. DOUBLY FED INDUCTION GENERATOR
The mechanical energy is converted to electrical energy by using an induction generator. The induction
generator used in this model is wound rotor induction motor in rotor reference frame. The electrical energy is
directly fed to the grid. The advantage of using dfig is control can be given from both stator and rotor sides. It is
connected by using a back-to-back, ac-dc-ac converter through a dc link capacitor. The purpose of dc link
capacitor is to maintain dc link voltage constant [4].as the induction generator used here is of variable speed
type it is flexible to use.IGBT’s are used in the converter model.
IV.PITCH CONTROL
The pitch control is used to extract possible power from wind for slow speed range of wind turbine. For high
speeds it becomes difficult to control the power.so, modern control methods such as IPC and fuzzy control are
used for extracting maximum power from wind. Such a scheme used previously with antiwindup is shown in
Fig.2.the antiwindup term prevents the integrated power error from accumulating when the rotor is operating
under low speed range. Its value is zero when pitch control is not saturated. Its value depends on turbine [5]-[7].
The PI controller can be replaced by fuzzy control which gives much flexibility and very accurate results. The
oscillations in the power output produce flickers in the dc output voltage. By using conventional pitch control
and individual pitch control the flickers can be reduced. The alternative to PI-controller is fuzzy control. The
latest technique in fuzzy is adaptive neuro fuzzy inference system (ANFIS).Kw shown in Fig.2.is antiwindup
term which depends on turbine.
Ki 1/s
Kw
Kp
Pg
+
-
Pgref
ᵝmax
ᵝmin
+
-
+
+
+
-
Fig.2.PI controller with antiwindup
ᵝ

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The results for mechanical torque, pitch and rotor speed without ANFIS control are shown in Fig. (3)- (4):
Fig.3.mechanical torque without ANFIS control
Fig.4.pitch control without ANFIS control

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V.ANFIS
The adaptive neuro fuzzy inference system (ANFIS) has become most popular method in the control area. It has
inputs, membership functions, rules and output [4].the computation is done by gradient vector method. The
suggested ANFIS has several properties:
1. The output has zeroth
order sugeno-type system.
2. It has single output system.
3. All output membership functions are constant.
4. It has no rule sharing.
5. It has unity weight for each rule
The simulink diagram with ANFIS with speed error as input is shown in Fig.6.
Fig.5.rotor speed without ANFIS control
Fig.6.simulink with ANFIS with speed error as input

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The simulink diagram with ANFIS with difference of speed error and power error (dw-dp) as input is shown in
Fig.7.
VI. RESULTS AND DISCUSSIONS
The pitch characteristics are much enhanced for (dw-dp) as input. The characteristics comparison of Tm and
pitch for the two types of inputs are shown in Fig. (8)- (9).
Fig.7.simulink with ANFIS with difference of speed error and power error
as input
Fig.8. mechanical torque characteristics comparison for both types of input

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The data for ANFIS is given in table.1.as shown.
INPUT OUTPUT
0.0100 19.9000
0.0200 18.8000
0.0300 17.7000
0.0400 16.6000
0.0500 15.5000
0.0600 14.4000
0.0700 13.3000
0.0800 12.2000
0.0900 11.1000
0.1000 10.0000
As shown in Fig. (9)- (10), the characteristics of Tm and pitch are much enhanced with the two types of
inputs compared to without ANFIS controller.
VII. CONCLUSION
The characteristics comparisons of pitch, Tm are compared with and without ANFIS control. With ANFIS
control two types of inputs are given i.e. with speed error as input and difference of speed error and power error
as input. The characteristics are much enhanced with difference of speed error and power error as input
compared to the speed error as input. Entire simulation is conducted on mat lab/simulink environment.
References
[1] K.Anusha, S.Sivaprasad,”An enhanced pitch control using fuzzy logic for stability improvement in dfig based wind energy system,
“international journal of innovative science, engineering and technology,vol.2,issue 10,October 2015.
[2] C.Veeramani, G.Mohan,”A fuzzy based pitch angle control for variable speed wind turbines, “international journal of engineering and
technology, vol.5, issue 2, may 2013.
[3] Abdel Ghani AISSAOUI,Ahmed Tahour, Mohamed ABID,Najib ESSOUNBOULI,Frederic NOLLET,”Power control of wind turbine
based on fuzzy controllers, “energy procedia,pp.163-172,nov.2013.
[4] Ramadoni Syahputra, Indah SOESANTI,”dfig control scheme of wind power using ANFIS method in electrical power grid system,”
international journal of applied engineering research, vol.11, pp.5256-5262, nov.2016.
[5] Yunqian Zhang, Zhe Chen, Weihao Hu, Ming Cheng,”flicker mitigation by individual pitch control of variable speed wind turbines with
dfig,”IEEE transactions on energy conversion, vol.29, issue 1, march 2014.
[6] G.D.RAI,”non-conventional energy sources,”khanna publishers, 2011.
[7] P.S.Bimbra,”generalized theory of electrical machines,”khanna publishers, 2013.
Fig.9. pitch characteristics comparison for both types of input
TABLE.1. data for ANFIS