Fuzzy and DTC Control Methods of Doubly Fed Induction Generator · 2017-04-13 · Controlling...

Vol. 3(9) Apr. 2017, pp. 635-648

635

Article History: DOI: JKBEI /10.112679 Received Date: 23 Nov. 2016 Accepted Date: 17 Mar. 2017 Available Online: 04 Apr. 2017

Fuzzy and DTC Control Methods of Doubly Fed Induction Generator

Omid Rahmani 1, Parviz Amiri 2, Zahra Mokhtari 3, Zhale Amirjamshidi 4

1Research Laboratory of Industrial circuits & systems, Shahid Rajae teacher training University, Iran




*Corresponding Author's E-mail: [email protected]

Phone Number: +98-021-66628597

Abstract

n this paper, we study the direct torque control method for controlling a double-fed induction

generator. In direct torque control method, high ripples are produced in torque and flux linkage, due

to the use of hysteresis controller and improper position of switching voltage source inverter space

vector. To solve this problem, fuzzy controllers are used to select the most suitable switching voltage

space vector for minimizing the torque ripples and flux linkage. However, fuzzy controllers resist against

sudden load changes. Also, due to the capability of fuzzy logic in solving complex and inaccurate

problems and in order to improve the speed control of the double-fed induction generator, fuzzy logic

are used to design the speed controller. In this paper, both control methods, the direct torque control and

improved direct torque control with fuzzy logics, are implemented based on stator flux oriented control

requirements. Nevertheless the simulation is developed in Matlab/Simulink using accurate generator

parameters.

Keywords: DFIG; Doubly-fed induction generator; Back to back converter; Direct Torque Control; Fuzzy control.

1. Introduction

The wind power capacity has substantially grown worldwide during the last few years [1]. Nowadays, the use of DFIG is ideal due to flexibility, strength and being multi-capacity; thus, it becomes one of the most applicable methods for utilization of wind energy. Working according to DFIG technology, it still confronts with various challenges such as stability of the system, the quality of power [1,2], low voltage, currency errors, phase imbalance [3] and Maximum Power Point Tracker (MPPT) [4]. (Factor of) control plays an important role in drives and thus in wind turbines technology. Controlling Doubly Fed Induction Generator (DFIG) is necessary for producing energy by wind turbines. (Factor of) control, for producing effective energy, keeps generator amounts like Torque, active and reactive power, and also amounts related to grid side converter like reactive power and DC Link Voltage, close to the optimum amount. Similarly, control along with modulator, if applied, have the duty of producing change over switch pulses according to reference desired amounts. Various methods have been presented for control including simple controllers with somehow high error and slippage to complex and flexible controllers such as Direct Torque Control (DTC) [5, 6] and the intelligent methods like fuzzy control [7]. Current paper examines three control methods for Doubly Fed Induction Generator used in wide range including: fuzzy logic and Direct Torque Control (DTC).

I

mailto:[email protected]

Omid Rahmani et al. / Vol. 3(9) Apr. 2017, pp. 621-648 DOI:

JKBEI/10.112679

636

Journal of Knowledge-Based Engineering and Innovation (JKBEI) Universal Scientific Organization, http://www.aeuso.org/jkbei

ISSN: 2413-6794 (Online) and ISSN: 2518-0479 (Print)

In controlling Doubly Fed Induction Generator based turbines, significant attention should be paid on voltage operation. Designed control, despite normal operation of the wind turbine, should be prepared for confronting network voltage disturbances. These disturbance, depend on the electrical network itself, make take different forms (like voltage drop, instability, harmonics, etc.). The main disturbance most countries are facing during using wind energy is Flux drop, which it is not solved, can result in network instability, breakdown of turbines and other serious problems.

In this paper, firstly Doubly Fed Induction Generator Model and wind model is presented, then RSC controller based on sensing stator flux and DTC torque control is presented. Fuzzy controller such as Introduction fuzzy logic, Fuzzy method schematic for DFIG, improve direct torque control method with fuzzy controller, Simulation, Simulation results is studied. At the end the compare two method and conclusion will be presented.ز

2. Doubly Fed Induction Generator Model

The reason of naming this generator as Doubly Fed Induction Generator (DFIG) is electrical transferring of electricity into network through both Stator and Rotor. Doubly fed induction generators have a relative better performance than other induction generators. Some of their advantages are controlling active and reactive power, controlling voltage, controlling network-dependent frequency, and not using a capacitor bank for providing required active power of induction generator. Figure 1 illustrates a variable speed wind turbine equipped with doubly fed induction generator. As it is shown, rotor circuit is equipped with two convertors and a DC link capacitor. The convertor is aligned directly to the rotors windings is RSC rotor convertor and the convertor is aligned directly to the network is GSC Convertor.

Grid-Side

Control

DFIG-Side

Control

Wind speed

DFIG

Wind

Wind Turbine

Inverter Rectifier

AC

filter

DC

Bus

Grid

Transformer

P , Q rotor

P , Q Stator

Pitch

Control

Gear

Box

Figure 1: Wind Turbine equipped with DFIG induction Generator.

According to the [8], wind-produced energy in wind turbines equipped with DFIG may have a 20% increases over other variable speed wind turbines and 60% over fixed speed wind turbines.

1.1. Wind Model

Power of kinetic Energy in wind can be calculated through (1) [9]:

𝑃𝑣 =1

2𝜌𝐴1𝑉𝑤

3

(1)

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Where, Pv is the wind power, ρ is the air density, A1 is the swept area, and Vw is the wind speed. Wind turbine can only recover some part of this power (2) [9]:

𝑃𝑚 =1

2𝜌𝜋𝑅2𝐶𝑝(𝜆, 𝛽)𝑉𝑤

3

(2)

Where, Pm is the output power, R is the radius of the turbine blades, λ is the blade tip speed ratio, and β is the blade pitch angle.

For a wind turbine, power coefficient of Cp is a function of wind speed, Wind turbine rotation

speed, and Pitch angle (3) [9].

𝐶𝑝 = 0.22 (116

𝜆𝑖− 0.4𝛽 − 5) 𝑒

−12.5

𝜆𝑖 (3)

Cp is often a function of the tip speed ration to the wind speed and λi is defined as (4) [9]:

𝜆𝑖 = [(1

𝜆+0.008𝛽) − (

0.035

𝛽3+1)]

−1

(4)

And λ is calculated thorough (5) [9]:

λ =𝜔𝑚𝑅

𝑉𝑤

(5)

Where, ωm is the turbine rotor angle speed.

Power coefficient curve 𝐶𝑝(𝜆, 𝛽) for a specific speed is provided as 𝜆. Constant Coefficients of

C1 and C2 are given by the turbine builder. It should be noted that the amount of 𝐶𝑝(𝜆, 𝛽)should

not be exceeded from its maximum amount, 𝐶𝑝𝑚𝑎𝑥=0.59, which is called Betz Limit.This coefficient

is used for constant performance of turbine. Diagram block of above equations is shown in Figure 2. In this block, turbine blade angle is considered constant (𝛽 = 0)[10].

r

wv

R

Landa

325.0 wPairm vCRP

pC

Figure 2: Dynamic model of wind turbine.

3. RSC controller based on sensing stator flux



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The stator flux reference frame, d axis is in the direction stator flux linkage vectorψs, is shown in figure 3 by name ψds = ψs andψds = 0 [11].

qsi

dsi

sds

sqs

0qs

dss

sd

ed

e

sq

eq

e

Figure 3: Phasor diagram by stator flux control [11].

Stator flux by using relevance machine in inertial reference system (ds − qs) in obtained by equation 6 and 7 [11].

𝜓𝑑𝑠𝑠 = ∫ 𝑉𝑑𝑠

𝑠 − 𝑅𝑠𝑖𝑑𝑠𝑠

(6)

𝜓𝑞𝑠𝑠 = ∫ 𝑉𝑞𝑠

𝑠 − 𝑅𝑠𝑖𝑞𝑠𝑠

(7)

Multiplying the dynamic equations of the induction machine in ωb obtained dynamic equations in terms of flux [11].

𝑉𝑑𝑠 = 𝑅𝑠𝑖𝑑𝑠 − 𝜔𝑒𝜓𝑞𝑠 +𝑑𝜓𝑑𝑠

𝑑𝑡

(8)

𝑉𝑞𝑠 = 𝑅𝑠𝑖𝑞𝑠 − 𝜔𝑒𝜓𝑑𝑠 +

𝑑𝜓𝑞𝑠

𝑑𝑡

(9)

𝑉𝑑𝑟 = 𝑅𝑠𝑖𝑑𝑟 − (𝜔𝑒 − 𝜔𝑟)𝜓𝑑𝑠 +𝑑𝜓𝑑𝑟

𝑑𝑡

(10)

𝑉𝑞𝑟 = 𝑅𝑠𝑖𝑞𝑟 + (𝜔𝑒 − 𝜔𝑟)𝜓𝑞𝑠 +𝑑𝜓𝑞𝑟

𝑑𝑡

(11)

Where

𝐹𝑖𝑗 = 𝜓𝑖𝑗𝜔𝑏

(12)

𝜓𝑑𝑠 = 𝐿𝑠𝑖𝑑𝑠 + 𝐿𝑚𝑖𝑑𝑟

𝜓𝑞𝑠 = 𝐿𝑠𝑖𝑞𝑠 + 𝐿𝑚𝑖𝑞𝑟

𝜓𝑑𝑟 = 𝐿𝑠𝑖𝑑𝑟 + 𝐿𝑚𝑖𝑑𝑠

𝜓𝑞𝑟 = 𝐿𝑠𝑖𝑞𝑟 + 𝐿𝑚𝑖𝑞𝑠

If stator flux reference frame was Inertial, stator flux gives the following results:



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𝜓𝑑𝑠 = 𝜓𝑠

(13)

𝜓𝑞𝑠 = 0

By substituting equation 12 in equation 7 to 11, new equations can be obtained from below:

{𝑉𝑑𝑠 = 𝑅𝑠𝑖𝑑𝑠

𝑉𝑞𝑠 = 𝑅𝑠𝑖𝑞𝑠 + 𝜔𝑒𝜓𝑑𝑠

(14)

𝑖𝑞𝑠 =−𝐿𝑚

𝐿𝑠𝑖𝑞𝑟

(15)

𝑖𝑞𝑠 =

−𝐿𝑚

𝐿𝑠(𝑖𝑚𝑠 − 𝑖𝑞𝑟)

(16)

Where

𝑖𝑚𝑠 =𝑉𝑞𝑠−𝑅𝑠𝑖𝑞𝑠

𝜔𝑒𝐿𝑚

(17)

𝑃𝑠 =

3

2(𝑉𝑑𝑠𝑖𝑑𝑠 + 𝑉𝑞𝑠𝑖𝑞𝑠)

(18)

𝑄𝑠 =3

2(𝑉𝑞𝑠𝑖𝑑𝑠 + 𝑉𝑑𝑠𝑖𝑞𝑠) (19)

By substituting equations 14,15 and 16 in about 17 or 18 active and reactive power is calculated as follows.

𝑃𝑠 =−3

2

𝐿𝑚2

𝐿𝑠𝜔𝑒𝑖𝑚𝑠𝑖𝑞𝑟

(20)

𝑄𝑠 =

3

2

𝐿𝑚2

𝐿𝑠𝜔𝑒𝑖𝑚𝑠(𝑖𝑚𝑠 − 𝑖𝑑𝑟)

(21)

By substituting equations 15,11and 16 in equation 9 and 10 have:

𝑉𝑑𝑟 = 𝑅𝑟𝑖𝑑𝑟 + 𝜎𝐿𝑟𝑑𝑖𝑑𝑟

𝑑𝑡− (𝜔𝑒 − 𝜔𝑟)𝜎𝐿𝑟𝑖𝑞𝑟

(22)

𝑉𝑞𝑟 = 𝑅𝑟𝑖𝑞𝑟 + 𝜎𝐿𝑟

𝑑𝑖𝑞𝑟

𝑑𝑡− (𝜔𝑒 − 𝜔𝑟) (

𝜎𝐿𝑟𝑖𝑞𝑟+𝐿𝑚2 𝑖𝑚𝑠

𝐿𝑠)

(23)

Where

𝜎 = 1 −𝐿𝑚2

𝐿𝑠𝐿𝑟

Equation 19 and 20 state that, Ps and Qs can be set independently with the rotor current components ,iqr and idr . thus the reference value iqr and idr is calculated by output loop power control.

If the Rs were regardless, that was acceptable for high power machine; final active and reactive power equation is equaled to [11]:

𝑃𝑠 =−3

2

𝐿𝑚

𝐿𝑠𝑖𝑞𝑟

(24)



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𝑄𝑠 =3

2

𝐿𝑚

𝐿𝑠(

𝑉𝑞𝑠

𝜔𝑒𝐿𝑚− 𝑖𝑑𝑟)

(25)

Where vqs is constant and equal to stator voltage Equation 21 and 22 is designed

By internal current control loop that using PI controller. Internal control loop design details are not explained here [12].

𝑉𝑑𝑟 = (𝐾𝑝 +𝐾𝑖

𝑠) (𝑖𝑑𝑟

∗ − 𝑖𝑑𝑟) − 𝑠𝜔𝑒𝜎𝐿𝑟𝑖𝑞𝑟

(26)

𝑉𝑞𝑟 = (𝐾𝑝 +

𝐾𝑖

𝑠) (𝑖𝑞𝑟

∗ − 𝑖𝑞𝑟) − 𝑠𝜔𝑒 (𝜎𝐿𝑟𝑖𝑞𝑟 +𝐿𝑚

𝐿𝑠𝑖𝑚𝑠)

(27)

Ki and Kp are respectively integral coefficients and proportional of PI controllers.

4. Controllers of Doubly Fed Induction Generators

4.1. DTC torque control

4.1.1. Introduction on DTC

Direct control techniques have considerable capability in applying certain voltage to system by digital electronic controller converters in a small portion of time. Ignoring the strengths of device, Figure 4 shows the simple stator and rotor DFIG circuits that are respectively connected to RSC system. λS stator flux is directly produced by the system voltage and the size and angle of rotor flux could be completely controlled by applying appropriate voltage to rotor circuit using RSC.

DFIG electric torque (Te) and flux (e.g. active and reactive power) are obtained based on controlling

the angle between stator and rotor flux and rotor flux vector γ and rotor magnetic flux |λr|.

1S 2S 3S

1S 2S 3S

E

GRID

r

s

Figure 4: Simple DFIG stator and rotor circuit.

Some of the main specifications of DTC strategy are as follow:

1) DFIG electric torque is proportionate to multiplication of λS and λr. In (24), the constant coefficient k depends on the parameters of the device [13].

|𝑇𝑒| = 𝐾|𝜆𝑠||𝜆𝑟| 𝑠𝑖𝑛(𝛾)

(27)

2) Eight allowed switch combination (S1, S2, S3 S1, S2, S3⁄ ) produce six active voltage vectors and two reactive vectors (zero) in RSC (as shown in 25). The variation of rotor flux in the voltage vector space is shown in Fig. 4 [13]. A 60-degree electric part (n = 1, …., 6) is associated to each active vector.



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𝑣𝑛 = 3𝐸(𝑠1 + 𝑠2𝑒2𝜋𝑗 3⁄ + 𝑠3𝑒4𝜋𝑗 3⁄ ) 2⁄ ; 𝑛 = 0, . . ,7

(28)

Ignoring rotor strength, the variation of rotor flux ∆λS in nth space vector vn applied in small time interval of ∆t is obtained from (29).

In discontinuous state, the next status of rotor flux is estimated from (30).

∆𝜆𝑟 = ∆𝑡. 𝑣𝑛

(29)

𝜆𝑟(𝑘 + 1) = 𝜆𝑟(𝑘) + ∆𝜆𝑟

(30)

Figure 5 shows the explained issues.

0114v

0103v 1102v

1001v

1016v 0015v 1kr

kr

Sector1

Sector6

r

Figure 5: Application of rotor flux ∆λr variation as voltage vector in v3 space to rotor circuit through RSC.

Depending on the situation of rotor flux and magnetic variations in Teand λr, and optimized voltage vector will be obtained to be applied on the rotor terminal. Thus, DTC is implemented using Table 2 and (31) and (32) [13].

Table 1 has three inputs and one output. The inputs includes variation in rotor flux (λr+ or λr−), torque variations (Te+ or Te− or Te=) and the flow part where the rotor flux flows from. The output is improvement of special vector that is applied on RSC.

Table 1: The selection of DTC optimum vector

Sector Torque

Ref

Flux

Ref 6 5 4 3 2 1

v1 v6 v5 v4 v3 v2 Te+

λr+ v7 v0 v7 v0 v7 v0 Te=

v5 v4 v3 v2 v1 v6 Te−

v2 v1 v6 v5 v4 v3 Te+

λr− v0 v7 v0 v5 v0 v7 Te=

v4 v3 v2 v1 v6 v5 Te−

The required variations of rotor and torque are obtained from (31) and (32).



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𝜆𝑟+ → |𝜆𝑟∗ | ≥ |𝜆𝑟| + ∆𝜆𝑟

(31)

𝜆𝑟− → |𝜆𝑟∗ | ≤ |𝜆𝑟| − ∆𝜆𝑟

𝑇𝑒+ → 𝑇𝑒∗ ≥ (𝑇𝑒 + ∆𝑇𝑒)

(32)

𝑇𝑒− → 𝑇𝑒∗ ≤ (𝑇𝑒 − ∆𝑇𝑒)

𝑇𝑒= → (𝑇𝑒 + ∆𝑇𝑒) ≥ 𝑇𝑒∗ ≥ (𝑇𝑒 − ∆𝑇𝑒)

Such that λr+ is used when positive variation is required in the rotor flux. For example, the size of reference flux |λr

∗| is bigger than or equal to sum of real value of flux |λr| and the predetermined threshold of ∆λr flux.

Similar argumentation is used for the remaining expressions. It should be mentioned that ∆Te is the related torque threshold.

Precise estimation of moment electrical torque and rotor flux leads to successful implementation of DTC.

The size of rotor flux |λr| and the orientation angle are obtained from (33). Moreover, electrical torque is obtained from (34).

|𝜆𝑟| = √𝜆𝑞𝑟′2 + 𝜆𝑑𝑟

′2 ; ∠(𝜆𝑟) = 𝑡𝑎𝑛−1(𝜆𝑞𝑟′ 𝜆𝑑𝑟

′⁄ )

(33)

𝑇�̂� = 3𝐿𝑀 (𝑖𝑞𝑠𝑖𝑑𝑟

′ − 𝑖𝑑𝑠𝑖𝑞𝑟′ ) 2⁄

(34)

4.1.2. DTC schematic based on DFIG

Figure 6 [13] shows complete strategy of DTC. The output of Table 1 directly specifies on/off states

of RSC switches and just one transfer reference is required for stator flow for transforming abc

constant components to dq rotating components.

m

DFIG

pP

sabi _

rabi _

r

r

r

*r

*eT

ab

abdq

r

r

eT

+

+

-

-

Figure 6: DTC Schematic based on DFIG.

4.1.3. Stimulation of system with DTC



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Block diagram of DTC controller is as Figure 7 The values of stimulated DFIG have been presented in

Table 1 and the stimulation has been done in dq reference. The DTC block diagram is obtain from

Figure 6.

Figure 7: DTC control block diagram based on Matlab simulation.

4.1.4. Simulation results of DTC method

Figure 8 shows the simulation results for DTC strategy. The values of simulated Doubly Fed Induction

Generator (DFIG) have been given in Table 2 [13]. The simulation has been performed in dq reference.

Table 2: The selection of DTC optimum vector

3KW Induction generator power rating

0-50 Hz frequency

Rs = 12.5 Ω Stator resistance

Ls = 23.3 mH Stator leakage inductance

Rr = 3.9 Ω Rotor resistance

Lr = 23.3 mH Rotor leakage inductance

Lm = 477 mH Magnetizing inductance

Pp = 2 Bipolar Machine

DC=120 V Line Voltage

PMAX = 0.75 kw Maximum Power



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Figure 8: a) Active power; b) Reactive Power; c) Rotor Voltage; d) Rotor current; e) Stator current; d) Torque

4.2. Fuzzy logic Control

4.2.1. Introduction to fuzzy logic

Fuzzy logic is one of the strongest control methods. Fuzzy control has 3 stages. Fuzzy function is

deduction of fuzzy diagram [14]. In the first part, functions are drawn based on specialized knowledge

(Figure 9). In this paper, fuzzy logics have been used to design a speed controller.

Rules Base

Control

Rules

Evaluation

DefuzzificatorPI Speed

Control

fuzzificatort

S

1

LT

*eT

Decision Conclusion

Permises

e

Figure 9: Block diagram of fuzzy logic.

4.2.2. Fuzzy sets

In this paper, the speed error input (the error rate of motor speed to reference speed) and torque,

are considered as fuzzy controller inputs, as shown in Fig. 9. The output of this torque controller is the

reference value.

For inference and non-fuzzy output values, the Stone-Weierstrass model has been used, as shown

in the following equation [15].



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𝑓1(𝑥) =

∑ �̅�𝑀1𝑙=1

1𝑙[∏ 𝑎𝑛

𝑖=1 1𝑖𝑙𝑒𝑥𝑝((

𝑥𝑖−�̅�1𝑖𝑙

𝜎1𝑖𝑙 )

2

)]

∑ [∏ 𝑎𝑛𝑖=1 1𝑖

𝑙𝑒𝑥𝑝((𝑥𝑖−�̅�1𝑖

𝑙

𝜎1𝑖𝑙 )

2

)]𝑀1𝑙=1

(34)

4.2.3. Improved direct torque control method with fuzzy controller

In fact, fuzzy control is vector control which fuzzy logic based on vector controller (Figure 10) is used

to track possible created error in grid [7].

*

*eT

LT

eT

*s

s

s

ba vv ,

dc

VFuzzy

Logic

Controller

Hysteresis

Comparator

Switching

Table

ba ii ,

Hysteresis

Comparator

Flux &

Torque

Estimation

DFIG

RSC

Figure 10: Block diagram of improved direct torque control method with fuzzy controller.

4.2.4. Stimulation of system with Fuzzy

Block diagram of fuzzy controller is as Figure 11. The values of stimulated DFIG have been presented

in Table 2 and the stimulation has been done in dq reference.



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Figure 11: Fuzzy control block diagram based on Matlab simulation.

Blocks, that have been named fuzzy control, are fuzzy controllers, before the controllers, two

subtractors have been placed included to subtract the reference rotor current, ird and irq, from the

rotor currents in the d and q axes for calculating the tracking current error. Due to multiplying of errors

by the fuzzy system efficiency, the currents may be obtained large values. Therefore, they are

multiplied by gain so that their amplitudes value is reduced. The inside of fuzzy blocks is as Figure 12.

Figure 12: Inside of Fuzzy control block.

Fuzzy system (Figure 12) includes two inputs that one of them is the tracking error inputs which are

multiplied by kp (proportional gain) and another is the integral of the tracking error input which is

multiplied by ki (integral gain). Also, because the inputs are PI, this kind of fuzzy system is called PI-

like-fuzzy system.

Figure 13: Fuzzy membership a) input 1 W b) input 2 delta c) output torque.

Simulation is done in Simulink/Matlab environment. At first, the simulation of the wind turbine

induction generator is firstly separated from the grid in the Matlab software. The parameter values of

doubly-fed induction generator.



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4.2.5. Simulation results of improved direct torque control method with fuzzy controller

Figure 14 shows the simulation results for Fuzzy strategy. The values of simulated Doubly Fed

Induction Generator (DFIG) have been given in Table 3[2]. The simulation has been performed in dq

reference.

Figure 14: a) Active power; b) Reactive Power; c) Rotor Voltage; d) Rotor current; e) Stator current; d) Torque

5. Result and compare

As it seen in Fig. 8-f, DTC control method has a very good response without any swing in torque

curve and offers the appropriate torque to the system. While in Fig. 14-f, torque curve in fuzzy control

method after 7 second reached to the steady state.

Fig. 8-c and Fig. 14-c shows the results of stimulation for stator current parameter with two

methods, DTC and fuzzy strategy. In Fig. 8-c and Fig. 14-c, the generator worked in nominal case. As it

seen, stator current in fuzzy reached to the stability in 6 seconds, while in DTC method, generator

worked in nominal without load. Nominal load applied in 0.1 second and in 2 second it decreased and

stator current became stable.

In Fig. 14-c, rotor voltage after 7 second was stable that is not acceptable. In DTC, system had swing

and the load was changed in 0-2 second. After 2 second it became stable.

Conclusion

In this paper, Direct Torque Control method is used. This method does not need to measure the

rotor quantities which have variable and sometimes very low frequency and create high latency. In

addition, this control method is also very close to the behavioral nature of the induction machine and

the torque production in this machine. Due to the nonlinear behavior of the induction machine and

wind turbine, using a fixed control factor will not have the favorable response. For this reason, the



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control factor of speed loop replaced the fixed factor as fuzzy and proportionate with two variables,

the speed and the speed loop error. Simulations showed that the fuzzy controller could present a

proper response to start-up and normal operation. Fuzzy logics are used in solving complex problems

for many years. Today the induction generator is one of the most widely used electrical drives and has

a remarkable role in industry. In this paper, a fuzzy controller has been used in order to improve the

speed and torque response errors. Results have been analyzed several times and for different speeds.

Results show that fuzzy controller is favorable in very fast and accurate application.

Appendix

The following abbreviations are used in this manuscript:

DFIG: Doubly Fed Induction Generator

MPPT: Maximum Power Point Tracker

DTC: Direct Torque Control

RSC: Rotor Side Convertor

GSC: Grid Side Convertor

PI: Proportional-Integral

References

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[3] Xiang, D.; Ran, L.; Tavner, P. J.; Yang, S. Control of a doubly fed induction generator in a wind turbine during grid fault ride-through. IEEE Trans. Energy Conversion 2006, vol. 21, no. 3, pp. 652–662.

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[13] Tremblay, E.; Atayde, S.; and Chandra, A. Comparative Study of Control Strategies for the Doubly Fed Induction Generator in Wind Energy Conversion Systems: A DSP-Based Implementation Approach. IEEE Trans. Sustainable Energy, vol. 2, no. 3, pp. 288–299, (2011).

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