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Abstract--This document researches converter control of

DFIG-based wind turbine, focus on the problem of DC voltage stability. According to the traditional SVPWM control strategy, a new control strategy is presented. This new control strategy uses Fuzzy-PI control that can modify PI parameters in real-time, and using feedforward control strategy to predict variation trend which can decrease overstrike. A simulation model, which represents the converter of doubly fed induction generator system is built-in MATLAB, and simulation results which are under symmetry grid voltage fault condition are given that illustrate the excellent performance characteristics of the Fuzzy-PI compound control system. The new control strategy has concise structure and more stable performance. Compared with traditional control strategy, the new one will not increase hardware cost, so it will have a bright future in engineering application.

Index Terms--Fuzzy-PI, Feedforward control, Converter, DFIG, Wind turbine.

I. INTRODUCTION

n recent years, with the aggravating of power crisis, wind power and its related technology cause more and more

attention. Among these, doubly fed induction generator is the most popular one. Compared with Permanent Magnet Synchronous Motor(PMSM), It has a smaller converter capacity, lower cost, and can decouple active and reactive power. DFIG-based wind turbine converter consists of two parts, grid-side and rotor-side, as shown in Fig.1. Rotor-side converter connects to induction generator rotor winding, and grid-side converter connects to the grid. Two converters are linked by DC bus. Whether its voltage is stable, it will influence the dynamic performance of AC excited generator, so making sure that DC voltage is stability is the main control target of converter. Currently, most of grid-side converter control strategy uses d-axis active power current qi to regulate DC bus voltage, and uses q-axis reactive power

This work was supported in part by National Natural Science

Foundation of China (50807035), Shanghai Rising-Star Program (09QA1402400), Innovation Program of Shanghai Municipal Education Commission (11ZZ169), Shanghai Key Scientific and Technical Project (10110502100) and Leading Academic Discipline Project of Shanghai Municipal Education Commission (J51303).

C. Y. Yu is with School of Power and Automation Engineering, Shanghai University of Electric Power, 2588 ChangYang Road, Shanghai, 200090, China.(e-mail: ycyuan0@google.com)

D. D. Li is with School of Power and Automation Engineering, Shanghai University of Electric Power, 2588 ChangYang Road, Shanghai, 200090 China (e-mail: powerldd@yahoo.com.cn).

current qi to regulate power factor[3]. In addition, to enhance DC bus voltage antijamming capability, many papers pay attention to the improving of tracking speed of current di [4]. In paper [6], is used to improve response performance of , but this method will cause more disturbance.

Fig. 1 Wind power conversion system of DFIG

For getting a better dynamic performance, this paper improve control method, not only is the grid-side converter considered, but also is the rotor-side. According to Fuzzy-PI compound control theory, change rate of DC bus bias voltage and rotor-side load current are both taken into control

II. MATHEMATIC MODEL

Please use automatic hyphenation and check your spelling. Additionally, be sure your sentences are complete and that there is continuity within your paragraphs. Check the numbering of your graphics (figures and tables) and make sure that all appropriate references are included.

A. DFIG model

The mathematical model of DFIG in synchronous reference frame is given as Eq. 1 and Eq. 2. The model includes two sets of equations, i.e. One is the voltage equations given by Eq. 1, and another is the flux equations expressed by Eq. 2[5].

.

)(

)(

drrsqr

qrrqr

qrrsdr

drrdr

dssqs

qsSqs

qssds

dsSds

dt

diRu

dt

diRu

dt

diRu

dt

diRu

(1)

Fuzzy-PI and Feedforward control strategy of DFIG wind turbine Chengyuan Yu, Dongdong Li Member, IEEE

I

qi di

IEEE PES ISGT ASIA 2012 1569537679

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2

.

)(

)(

)(

)(

qsmqrmrkqr

dsmdrmrkdr

qrmqsmskqs

drmdsmskds

iLiLL

iLiLL

iLiLL

iLiLL

(2)

According to Eq. 1 and Eq. 2, the equivalent circuits of the dq- model in the synchronous reference frame are envisaged in Fig 2.

(a) d-axis equivalent circuit

(b) q-axis equivalent circuit

Fig. 2 Equivalent circuits of DFIG in the synchronous frame

B. Converter

Grid-side converter can be seen as a PWM rectifier, its main circus is shown in Fig.3.

, , are three-phase voltage

of power grid respectively. , , are the three phase current of AC side converter. is filter inductance, is DC side capacity, is DC bus voltage, is DC current, is DC side load current.

Fig. 3 Topology structure of grid side converter

The power electronics devices in Fig 3 are supposed as ideal switching devices, and AC filter inductance and resistance are linear. So the voltage balance function the inductors is

)(

)(

)(

0

0

0

NcNccfc

f

NbNbbfB

f

NaNaafa

f

vveiRdt

dil

vveiRdt

dil

vveiRdt

dil

(3)

Where , , is bridge arm voltage, is the voltage between negative bus and neutral point, is the line resistance of AC side. Switch function can be used to describe switch action of 3 bridge arms as

）（，，

，，cba

conductlowershutoffupper

shutofflowerconductupperS ,,k

0

1k

(4)

So, the bridge arm voltage can be shown as

dckN vsV k （k=a，b，c，） (5) Using the coordinate transformation. Eq. 3 Eq. 4 and Eq. 5

can be transformed into a dq reference frame as

load

q

d

dc

q

d

qd

q

d

dc

q

d

i

u

u

C

L

L

U

i

i

C

s

C

sL

s

L

RL

s

L

R

dt

dUdt

didt

di

100

01

0

001

02

3

2

3

(6)

Rotor side converter has the same structure as grid side converter, the only difference between them is control strategy. Main control traget of rotor side PWM converter is Maximum Power Point Tracking, It is meaning that the active power and reactive power can be control respectively. In this process, the induction generator is the control object, so the control strategy of rotor side PWM converter should be determined by operation characteristics of generator. To make mathematical model simpler, generator stator resistance is ignored, and in

steady condition, flux is constant( sP =0, qs =0),so we have

Eq. 7 as follow

s

drmdsdsns

qrs

msnes

qrss

mne

sdss

L

iLpQ

iL

LpTP

iL

LpT

U

1

11

1

(7)

In Eq. 7, and is the rotor inductance and stator inductance respectively. is stator mutual inductance, and

is d axis and q axis current respectively. ds and is d axis

and q axis linkage respectively, is the amplitude of stator linkage, is synchronous frequency, is pole pairs.

III. CONVERTER DESIGNS BASED ON FUZZY-PI COMPOUND

CONTROL

A. Traditional PI control strategy

The traditional grid side converter control strategy is direct current control strategy. Regulate can control active power of grid side converter, and the effect is reflected on the changing

of DC bus voltage. Regulating qi can control the reactive

power, and use state feedback to eliminate the coupling between d-q axis. Although the PI controller has many advantages like having no steady-state error, simple design, for frequent changes of load, due to PI parameters are fixed, the dynamic performance of converter is not good. To solve this problem, many domestic and foreign papers have proposed the use of Fuzzy PI control strategy. By change PI

ae be ceai bi ci

fl fCdcV di loadi

aNV bNV cNV 0NVfR

sL rLmL dri

qri

qs

1 snP

qi

2

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parameters in real time, the performance of converter is improved.

B. Fuzzy-PI compound control strategy

Whether it is conventional PI control or fuzzy PI control, the controller design is all based on bias control, which means that the controller will not respond before the DC bus feedback value and the reference value is inconsistent. This inevitably will bring about control system delay. And the change of load current is the main factor that affect bus voltage. Although feedforward of load current can improve anti-jamming capability, the inertance of system will reduce response speed. Based on the above issues, combined with double-fed wind turbine characteristics, the signal which shows the change trend of load current should be introduced into control system as reference inputs to compensate the inertance caused by control system. So that PI parameters can be modified in advance before bias of capacity voltage appear. The change rate of space vector of rotor current is chosen as input of feedforward compensation fuzzy PI controller. At the same time, the change rate of DC bus bias voltage is introduced as another input of fuzzy rule. And the control diagram is shown in Fig.4.

Fig. 4 Control diagram of Fuzzy-PI compound controller In Fig 1. E is DC bus bias voltage, dE/dt is the change rate

of E, ai , bi , ci are load side current as feedforward

compensation, which will influent DC bus voltage, dId/dt is the change rate of active power current of rotor side. The linguistic variables of dE/dt and dId/dt A are both {Z, S, M, B}, the discourse domain of dE/dt is {0, 0.02, 0.1, 0.2}, the

discourse domain of dId/dt is {0, 0.05, 0.1, 0.4}. pk and

ik are correction value of pk and ik which can modify PI

parameters. When disturbance is big, the proportional gain is increased suitably to accelerate response. And when disturbance becomes small, the integral gain is increased suitably to eliminate steady-state error. The discourse domain

of pk and ik are {0, 0.1, 0.2, 0.3} and {0, 0.05, 0.1, 0.15}

respectively. Triangle and Ladder-shaped Membership Functions are used in fuzzification. Inference Mechanism is

Mamdani, and Center of Gravity is used in defuzzication. Fuzzy inferential rule is shown in Table 1 and Table 2.

TABLE I

FUZZY INFERENCE OF pk

pk

dE/dt

B M S Z

dId/dt

B B B B B

M B B M M

S B M S S

Z B M S Z

TABLE Ⅱ

FUZZY INFERENCE OF ik

ik dE/dt

B M S Z

dId/dt

B Z Z S B

M Z S M M

S S S M S

Z S M B Z

IV. SIMULATION ANALYSIS

Simulations of the Fuzzy-PI compound control strategy for a DFIG-based wind power generation system is carried out using MATLAB/Simulink, and Fig 5 shows the scheme of the implemented system. Discrete models are used with a simulation time step of 10 s . And the converter is connected

to a 220V supply, frequency is set at 50Hz. The line inductance and resistance are 5mH and 0.05 respectively. The DC-link voltage was regulated at 500V. During simulations, a sampling frequency of 10kHz is used for the proposed control strategy. To get a better comparability, The

PI parameters of different control strategies are set to pk =0.4,

ik =5.

Fig. 5 Main scheme of the simulated system

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Several simulations have been carried out to study the performance of the converter in both traditional and Fuzzy-PI

compound control strategy.Fig 6 show response curves of dcV

controlled by different PI controller, with dcV set to 650V and

power flowing from the grid to the DC link.

(a) traditional control strategy

(b) Fuzzy-PI Feedforward control strategy

Fig. 6 Response curves of dcV controlled by two different PI controller

Fig 7 shows the response of dcV under symmetry grid fault

condition. Here the three-phase grid voltage is stepped from 1 P.U to 0.7 P.U at t=0.8s. Because the load current of rotor side can affect DC bus voltage, the change rate of it can predict the variation tendency of DC bus voltage and PI parameters can be modified in anticipation to decrease bus voltage drop. As shown in Fig 7, compared with traditional PI control, the Fuzzy-PI compound control have less overshoot and quicker response speed.

(a) traditional control strategy

(b) Fuzzy-PI Feedforward control strategy

Fig. 7 Response curves of dcV under symmetry grid fault condition

Fig 8 shows response curves of dcV under different

operations of the converter. When the DFIG-wind turbine is working in subsynchronous operation, the rotor gets active power from the grid, and the grid-side converter operats in rectifying mode. When rotate speed of rotor exceed synchronous speed, the rotor send active power back to grid through converter, the grid-side converter works as an inverter. The power that rotor sent back to grid will boost DC bus voltage, and make a overshoot. As shown in Fig 8, Fuzzy-PI

compound control have better performance, and can decrease overshoot voltage.

(a) traditional control strategy

(b)Fuzzy-PI Feedforward control strategy

Fig. 8 Response curves of dcV under different operations of the converter

V. CONCLUSION

This paper has presented a control strategy for improving the performance and stability of DC bus voltage of converter based on DFIG system. The behaviors of the converter have been described using a mathematical model. Simulated studies on converter are carried out to validate the proposed control strategies. As a result ,the following conclusion can be drawn.

(1) The Fuzzy-PI compound control strategy can change PI parameters by testing the bias of input variable quantity in realtime. At the same time, feedforward compensation techniques can greatly reduce the error caused by variable gain characteristic of load. This restrains the voltage fluctuation of DC bus. Simulation results prove that the new control strategy has ability to predict DC voltage disturbance, and have a better performance characteristic.

(2) This new control strategy do not need extra hardware, will not increase the cost of control system, and the control arithmetic is easy to compile in software. At the same time, because the robustness is enhanced, it is become possible to choose smaller capacity in converter,and this control strategy has good practicality in engineering.

VI. REFERENCES [1] Jung.J.-W. Choi,Y.-S, Leu.V.Q, Choi.H.H: Fuzzy PI-type current

controllers for permanent magnet synchronous motors. Electric Power Applications, IET,vol.5, Issue.1, pp. 143-152, January .2011

[2] Shun Chung Wang, Yi-Hwa Liu: A Modified PI-Like Fuzzy Logic Controller for Switched Reluctance Motor Drives. Industrial Electronics, vol. 58, no 5, pp. 1812-1825 May , 2011

[3] Precup.R, Preitl.S, Rudas.I.J, Tmescu.M.L,Tar.J.K: Design and Experiments for a Class of Fuzzy Controlled Servo Systems .Mechatronics, vol. 13, no. 1, pp.22-35, Feb 2008

[4] Ming Cheng, Qiang Sun, Zhou E: New self-tuning fuzzy PI control of a novel doubly salient permanent-magnet motor drive. Industrial Electronics, vol. 53, no. 3, pp. 814-821, 2006.

[5] Cardenas.R, Pena.R: Sensorless vector control of induction machines for variable-speed wind energy applications. Energy Conversion, vol. 19, no. 1, pp. 196-205, March 2004.

[6] Fan Xiaoxu, LV Yuegang, BAI Yan, etc: Modeling and simulation of the variable-frequency excitation power supply served for the wind power

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DFIG. Proceedings of the IEEE International Conference on Automation and logistics, September 7-8, 2008, Qingdao, China. 2008.

[7] Fouly,T.H.M, Zeineldin,H.H, Saadany,E.F, Salama, M.M.A: Impact of wind generation control strategies, penetration level and installation location on electricity market prices, Renewable Power Generation, IET, vol. 2, no.3, PP.162-169, Sep 2008.

[8] Rodriguez-Martinez, A., Garduno-Ramirez, R., Vela-Valdes, L.G.: PI Fuzzy Gain-Scheduling Speed Control at Startup of a Gas-Turbine Power Plant, Energy Conversion, vol. 26, no. 1, pp. 310-317, March 2011.

[9] Wei Qi , Jinfeng Liu , Xianzhong Chen , Christofides, P.D. : Supervisory Predictive Control of Standalone Wind/Solar Energy Generation Systems, Control Systems Technology, vol. 19, no. 1, pp. 199-207, Jan 2011.

VII. BIOGRAPHIES Cheng Yuan Yu was born in jiangsu,china,in1987,

and graduated with Bachelor’s degree in electrical engineering and automation from electrical engineering department, Nanjing Institute of Technology, Nanjing, china in 2009. Now he is one of Current Master students in Shanghai University of Electric Power, Shanghai, China. he is doing research on the wind power system of doubly fed induction generator for his Master’s Thesis uder the guidance of Prof.Li. His research interests include intelligent computation and algorithm analysis and design, and

wind power generation system.

Dong Dong Li was born in Anhui, China, in 1976. In 1998 and 2005, he received his BSc and PhD degrees from Zhejiang University and Shanghai Jiao Tong University, both in electrical engineering, respectively. He is now working as a professor in Shanghai University of Electric Power, Shanghai, China. From 1998 to 2000, he was with Wuhu power plant as an Electrical Engineer. His area of interest includes analysis of electric power system, power system planning and wind power generation system.

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