The Study of Strategy for Synchronous Wind Power Generation with Brushless Excitation System Pitch

Control

Meng Yanjing Wind power generation laboratory

Shaanxi University of Science & Technology Xi’an 710021, China mengyj@sust.edu.cn

Gong Wenzhan Wind power generation laboratory

Shaanxi University of Science & Technology Xi’an 710021, China

gong_wenzhan@163.com

Abstract—This paper analyzed the structure of a direct-drive wind power generation, The mathematical model of the wind power generation system is complex, strongly affected by the varying parameter and exterior disturbing, and has the features of nonlinear, time changing and coupling. According to the above characteristics, the fuzzy-logic control is introduced into the wind power system. By using the fuzzy controller as the controller of the variable-pitch system. This paper carries out simulation of the controller when the wind speed is higher than the rated speed. The result shows that the controller produces good dynamic performance, good robustness and adaptability.

Keywords-Synchronous Wind Power Generation With Brushless Excitation System; Power Control; Variable-Pitch Control; Fuzzy Adaptive PID Control

I. INTRODUCTION Wind power has low energy density, randomicity and

uncertainty characteristics， etc. Wind power systems become a nonlinear ， multivariable, strong coupling, unstable, hysteresis of complex dynamic system. But the generator is based on the steady-state, and motor parameters are not accurate and slowly changing characteristics, existing lag and random disturbance, it is difficult to controlled by linear conventional control scheme [1~2].

Pitch control is studied, in order to adopt different control strategies at different wind speed. Blow the rated wind, variable speed is adopted, controlling generator excitation current so that the system maximum utilization factor of wind energy. While, above the rated wind speed, pitch angle is controlled so that generator output power constant.

II. TOPOLOGICAL STRUCTURE OF GENERATOR Fig. 1 shows a synchronous wind power generation with

brushless excitation system with double PWM converter grid connection. The system consists of fan, the growth rate boxes, brushless synchronous generator, PWM converter, DC supporting capacitors, PWM inverter, LCL filter, transformer and other components.

Wind energy transforms mechanical energy through the fan and then through the brushless synchronous generator transforms electrical energy. At this time, power quality is unstable, and can not grid. AC-DC-AC process is completed by double PWM converter structure and LCL filter, then rose to the grid voltage by transformer can be supply.

Figure 1. Block diagram of synchronous windpower generation with brushless excitation system

III. UTILIZATION OF WIND Expression of the nonlinear model of fan

This work is supported by Science and Technology Department of Shaanxi Province Foundation (2008KW-14), and the Graduate Innovation Fund of Shaanxi University of Science and Technology.

978-1-4244-9857-4/11/$26.00 ©2011 IEEE

Y

N

Y

Y

Y

N

N

N

End

Start

β=90°,Fan can start, program self-inspection

V>3m/s

3m/s<V<12m/s

12<V<25m/s

V>25m/s

Fan cut out the grid

Activated brake devices brake

Start featheringservo motor

Fan Without Action

Running Fixed pitch

Running variable pitch

Whether the fault alarm

signal

Y

Whether meet the grid

conditions

Grid

3 2m

12.5/

3

T C ( , ) / 2

/C ( , ) 0.22(116 / 0.4 5)

1/ 1/ ( 0.08 ) 0.035 / ( 1)

p

p

R v

R ve δ

λ β ρπ λλ ω

λ β δ β

δ λ β β

−

⎧ =⎪

=⎪⎨

= − −⎪⎪ = + − +⎩

(1)

Where, Tm denotes Wind turbine torque，unit is N•m;ρdenotes air density, unit is kg/m3;R denotes rotor blade diameter, unit is m; v denotes wind speed, unit is m/s； Cp denotes fan of wind energy utilization coefficient，maximum is Bezier limitation (59.3%).However, it is difficult to achieve 0.5 in practice, about 0.4; β pitch angle, unit is °; λdenotes tip speed ratio; ω denotes angular velocity of wind rotor, unit is rad/s.

The utilization ratio of wind is shown in the Fig. 2

Figure 2. The utilization ratio of wind

IV. ACTIVE PITCH CONTROL Variable-pitch refers to using control technology change

pitch angle of installed in the wheel hub blades, changing the aerodynamic characteristics of blades. The blades and the whole of force have improved, and the output power can be improved at high wind speed.

Diagram of variable-pitch control system is shown in the Fig. 3.

When wind speed is lower than start wind speed(generally 3m/s),pitch angle is 90°.At this time, blades do not produce torque. When the wind turbine start up, pitch angle at a certain speed reduced to standby angle， about 3°; When the wind speed is lower than the rated speed, making the pitch angle maintained at 3 °, obtained maximum wind energy; When the wind speed above the rated speed, based on wind speed and the power feedback signal, the controller controls the servo motor to change pitch angle, making the wind generator output power rating. If the fan fails or accepts outage control command, blades can quickly feathering propeller to ensure fan safety and reliability.

Figure 3. The operation system diagram of variable-pitch

V. FUZZY ADAPTIVE CONTROL

A. The Basic Principle Of Fuzzy Control The basic principle of fuzzy control is shown in the Fig. 4,

fuzzy controller is the core of it, which shown in the figure

box.

Figure 4. Schematic diagram of fuzzy control system

Control rule of fuzzy controller realized by a computer program, The basic idea of fuzzy control is computer access the exact value of controlled by sampling interrupt, then this amount compare with the given value gets the error signal(denotes by e). Generally, choose the error signal and error change (denotes by ec) as inputs of fuzzy controller. The accurate values e and ec fuzzification into fuzzy values E and

EC. E and EC can use the corresponding fuzzy language to show, obtained the e and ec of fuzzy language collection subsets. Then the fuzzy subsets and fuzzy control rules (fuzzy relations) according to the compositionrule of fuzzy inference get fuzzy decision, volume of fuzzy control is

( )u e ec R= × ⋅ (2)

Where, u is a fuzzyquantity

Finally, the defuzzification process is employed, obtain exact control value, through the implementing agencies to control the controlled object[3].

Fuzzy control does not need exact mathematical model can be efficiently integrated experience and knowledge of experts has good dynamic performance and robustness. Especially, fuzzy control applies to parameters and structure exist large uncertainty factors or unknown control object. The wind generator is a typical volume complex nonlinear system, in addition to the size of wind speed and direction of random variation, also affected by the parameters of power fluctuations and the impact of atmospheric conditions and other factors[4].

B. Fuzzy Adaptive PID Control 1) The traditional PID

The traditional PID conventional regulator is usually based on linear theory, can be in only a feature operating point or limited range get better control, in order to obtain good dynamic performance and eliminate static error, PID controller gain calculation requires accurate Mathematical model. In PID control, Kp, Ki, Kd has the following characteristics[5]:

• When the proportional coefficient Kp increases, Response velocity quickened, system steady-state error decreases, control accuracy is improved, but Kp over the general assembly to make the system overshoot, or even lead to instability.

• Integral function is mainly to eliminate the static error of the system. Ki increases, tending to reduce the static error, but if Ki is too large, overshoot will increase, or even cause oscillation.

• Differential can improve the dynamic performance. Increase Kd, help to accelerate system response, the system overshoots decreases, and stability increased, but the inhibiting outside disturbance ability is abate. If Kd is too large, the adjustment process appears overshoot deceleration regulating time growth; Conversely, if Kd is too small, system response is slow, stability becomes poor.

Conventional PID control parameters Kp, Ki, Kd are mostly controlled by a certain performance requirements, and in accordance with the above characteristics, adjust a fixed set of parameters. If the setting improper, not only can not realize the control, but may cause divergent oscillation, seriously affecting the production process. In order to automatically adjust the PID parameters, this work designed a fuzzy inference device, according to e and ec adjusting Kp , Ki, Kd.

2) Design of fuzzy adaptive PID controller Fuzzy adaptive control theory is the fuzzy control theory

and adaptive control theory intersect, mutual penetration form a field of study.

Figure 5. Block diagram of Fuzzy Adapitve PID

Fuzzy Adaptive PID control of the basic process is: Based on the basic theory and method of fuzzy mathematics, uses fuzzy set said the conditions operation of the rules. And the rules and related information placed in a computer knowledge base, given the pitch angle, compared with measurements of the actual response, obtained error and error change ratio. Based on fuzzy reasoning, automatically adjusting for optimal PID parameters to meet the different time e and ec on self-tuning PID parameters required, obtained the optimal adjustment of pitch change targets[6].

Linguistic variables of fuzzy controller are: E、Ec、Kp、Ki、Kd；

The corresponding language values are:{NB，NM，NS，Z，PS，PM，PB}；

Domain of E and Ec is：{-3，-2，-1，0，1，2，3}；

Domain of Kp：{-0.3，-0.2，-0.1，0，0.1，0.2，0.3}；

Domain of Ki：{-0.06，-0.04，-0.02，0，0.02，0.04，0.06}

Domain of Kd：{-3，-2，-1，0，1，2，3}

In order to realize PID parameters(Kp，Ki，Kd)based on the change of E and Ec on-line modification, the corresponding relations between Kp，Ki，Kd and E，Ec must be found. According to expert experience, parameters are tuning fuzzy control rule table for Kp ，Ki ， Kd of the appropriate "IF E = ... AND EC = ..., THEN Kp, Ki, Kd = ..." are created, shown in Table 1 to Table 3.

TABLE I. FUZZY RULE TABLE OF KP

ec △Kp

e

NB NM NS Z PS PM PB

NB PB PB PM PM PS Z Z NM PB PB PM PS PS Z NS NS PM PM PM PS Z NS NS Z PM PM PS Z NS NM NM

PS PS PS Z NS NS NM NM PM PS Z NS NM NM NM NB PB Z Z NM NM NM NB NB

Wind Speed(m/s)

TABLE II. FUZZY RULE TABLE OF KI

ec △Ki e

NB NM NS Z PS PM PB

NB NB NB NM NM NS Z Z NM NB NB NM NS NS Z Z NS NB NB NS NS Z PS PS Z NM NM NS Z PS PM PM

PS NM NM Z PS PS PM PB PM Z Z PS PS PM PB PB PB Z Z PS PM PM PB PB

TABLE III. FUZZY RULE TABLE OF KD

ec

△Kd

e

NB NM NS Z PS PM PB

NB PS NS NB NB NB NM PS NM PS NS NB NM NM NS Z NS Z NS NM NM NS NS Z Z Z NS NS NS NS NS Z PS Z Z Z Z Z Z Z PM PB NS PS PS PS PS PB PB PB PM PM PM PS PS PB

VI. RESEARCH RESULTS In the simulation conditions, the air density using the

density of air at standard atmospheric pressure at room temperature(ρ= 1.225kg•m-3); Diameter of wind turbine rotor is 70m, liters ratio of gear case(denotes by N)is 90; Rated power of generator is 1.5MW

• For a determined wind turbine, the unit usually starts with wind speed at 3～4m/s. However, the mechanical response of the wind turbine is much slower than the electromagnetic response, the dynamic characteristics of power electronic devices are not included in the simulation model, so set the initial wind speed is 3.5m/s at low wind speed.

• When the wind speed(3.5m/s～11.5m/s) is lower than the rated speed(3.5m/s ～ 11.5m/s), β = 0° (pitch angle is generally maintained at -2°～+2°, 0°is usually),when λ =7, Cpmax=0.46, system gains the maximum wind energy utilization factor. Control exciter excitation current to control the excitation of wind power, to maintain maximum output voltage is constant, to reach the maximum wind energy utilization factor. Yaw system ensures wind turbines is always to wind direction.

• Above rated wind speed(11.5m/s～25m/s),in order to prevent frequent reciprocating change propeller, when the Power deviation in ±10kW not to change the propeller. Yaw system ensures wind turbines is always to wind direction.

• When the wind is higher than 25m/s, generator brake.

The simulation results are shown in Fig. 6 and Fig. 7.

Figure 6. The relationship between wind speed and pitch angle

Figure 7. Output power when the wind speed is higher than the rated speed

VII. CONCLUSION Fuzzy adaptive PID control theory is applied to

synchronous wind power generation with brushless excitation system pitch control. By the continuous detection of e and ec, based on the principle of fuzzy adaptive real-time online modification Kp, Ki, Kd, so that the controlled object has a good dynamic performance and improved wind power system pitch control effect. Above rated wind speed, pitch control through fuzzy adaptive PID controller to achieve, and output power remains constant, showing a good dynamic characteristics.

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