Energy and Power Engineering, 2013, 5, 6-10
doi:10.4236/epe.2013.53B002 Published Online May 2013 (http://www.scirp.org/journal/epe)
The Pitch Control Algorithm of Wind Turbine Based on
Fuzzy Control and PID Control
Rui Guo, Jinsong Du, Jinghui Wu, Yiyang Liu
Wind Power Control Technology Department, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China
Received 2013
ABSTRACT
Due to the special features of great inertia, pure lag and non-lin earity provided with wind turbine, coupled with complex
and variable working co nd ition, it is d ifficult to achiev e satis facto ry con tro l results simply b y employin g trad itio nal PID,
which has the drawbacks such as adjustment inconvenience, poor anti-interference, and large overshoot, and prolonged
adjust span. This paper puts forward one type of improved controller combining Fuzzy Con trol with PID Control. W ith
larger speed deviation, the controller emphasizes Fuzzy Control to speed system response; with less speed deviation, the
controller emphasizes PID Control to improve control accuracy. Simulation Test directing at the algorithm is based on
the Bladed software, with the positive result of improved dynamic and static performance of wind turbine under large
disturbance.
Keywords: Fuzzy Control; Pitch Control; Wind Turbine; Bladed
1. Introduction
In general, most current wind turbines, asynchronous
doubly-fed or synchr onous, adopt pitch contro l system to
ensure safe operation of wind turbines above the rated
wind speed and output steady rated power [1]. Pitch con-
trol system is considered to have two modes, Hydraulic
and Electric pitch-controlled system. No matter driven by
any of the two modes, the overall pitch co ntrol system is
regarded as actuating element in the generator revolving
closed-loop control system, implementing pitch angle
signal transmitted by the main control system. That is to
say wind turbine alters wind-power utilization coefficien t
by means of changing pitch angle to maintain stable
output power [2]. At present pitch controller is simple
PID control. Though receiving wide application in indus-
try field, PID control, confined by non-linearity charac-
teristic of wind turbine, makes it difficult to adjust pa-
rameters, which leads to untimely restraint of instability
caused by external disturbance. The above-mentioned
difficulty accordingly brings about a series of problems
such as unstable revolving speed, reduced generating
efficiency, and quickened wear process, etc. Although
some scholars bring forward the theory of applying multi
groups of PID parameters on the basis of pitch angle to
ensure rapidity and stability of pitch process, it is diffi-
cult to tune multi groups of parameters on site [3].
Intelligent control is based upon Control Theory, In-
formation Theory, Artificial Intelligence, Bionics, Neu-
rophysiology and Computer Science, and gradually de-
velops to advanced information and control technology.
Fuzzy Control is one kind of Intelligent Control. It
doesn’t need accuracy dynamic model, but uses artificial
control rule to organize control decision table, then
comes up wit h outputs.
The paper proposes one type of pitch control algorithm
of wind turbine based on Fuzzy Control and PID Control,
with flexible and adaptable advantages of Fuzzy Control
and accurate characteristic of PID Control. It helps to
work out traditional pitch control algorithm, conduct on
simulation test platform, and achieves desirable control
results.
2. Asynchronous Doubly-fed Wind Turbine
Pitch Control Strategy
There are two types of Asynchronous doubly-fed wind
turbine pitch control strategy based on different rotor
speed: above or below rated wind speed area.
In the area below the rated wind speed, the wind tur-
bine is designed to capture wind energy as much as pos-
sible, and usually with lower aerodynamic load com-
pared with above rated wind speed area. So the main
control system doesn’t issue an order to the p itch control
system, i.e. the wind turbines run at fixed pitch.
When it is above the rated wind speed, the rotor speed
has reached the rated speed area or above. Pitch system
should be effective in adjusting wind turbines to absorb

R. GUO ET AL. 7
wind power and reduce the blade load. The main control
system is needed to control the pitch control system,
thereby changing wind turbine wind-power utilization
factor, maintaining the stability of unit output power at
rating value.
Pitch control system controls the generator speed,
which makes wind turbines stabilize at the rated speed.
When the wind speed is relatively stable, p itch controller
requires control precision to ensure a smooth output rated
power. When the external wind speed increases suddenly,
pitch controller shou ld be able to increase the p itch angle
in high speed; when the external wind speed suddenly
decreased, pitch controller should be able to reduce the
pitch angle in high speed. Encountering grid failure cases
such as voltage drop causing torque drop, pitch controller
should respond to adjustment quickly, not only can in-
crease pitch angle in high speed to ensure no more than
the maximum speed, but also avoid off-grid due to de-
creased generator speed too much.
3. Design of Fuzzy-PID Pitch Controller
Wind turbines have the features of great inertia, pure lag,
and non-linearity, and with complex and variable work-
ing condition, it is difficult to achieve satisfactor y control
results simply by employing traditional PID, which has
the drawbacks such as adjustment inconvenience, poor
anti-interference, and large overshoot, and prolonged
adjust span. Over the last decade, Fuzzy Control is the
product of rapid development of Intelligent Control
technology. Because control rules are based on the ex-
perience of operator, it can receive desirable control re-
sults even without a precise mathe matical model. But the
nonlinear control decides it has static error.
This paper presents a controller combining Fuzzy
Control with PID Control, not only maintains the advan-
tages of PID Control, but also has the characteristics of
Fuzzy Control, and introduces a coordination factor used
to coordinate Fuzzy Control and PID Control for pitch
controller. Control block di agram is shown i n “ Figure 1”.
3.1. Design of Fuzzy Controller
According to the actual situation, wind turbine has a
rated speed of 1803 rpm, taking into account of the
maximum speed of 1950 rpm, the set allowable deviation
is [-100,+100]. Selecting the wind turbine revolving
speed deviation e and the deviation ratio ee as the fuzzy
controller input lingual variables E, EE. Selecting pitch
angle u as the fuzzy controller outpu t lingual variables U
(E, EE, U are e, ee, u after fuzzy alg orithm), thus consti-
tuting a dual-input and single-output fuzzy controller.
Figure 1 show that the fuzzy controller consists of three
function aspects: the fuzzy quantization and fuzzification
used for input signal processing, Fuzzy Control algo-
rithm function unit, and fuzzy judgment unit used for
defuzzification output.
3.1.1. C on f irm Fuzzy Set and Domain of Input and
Output
Selecting 8 lingual variables to define deviation E, mark
E1,E2,
… E8, error range is [-e,e]. Selecting 7 lingual vari-
ables to define the wind turbine revolving speed devia-
tion ratio EE, mark EE1,EE2,
… EE7, error range is [-ee,
ee]. Selecting 7 lingual variables to define output of the
Fuzzy Controller, the pitch angle, U, mark U1, U2…
U7.The Fuzzy set is:


,,,,,,,EPBPMPSPONONSNMNB (1)


,,,,,,EEPB PMPS O NSNMNB (2)


,,,,,,UPB PMPSO NSNMNB (3)
PB, PM, PS, PO, O, NO, NS, NM, NB, represent posi-
tive big, positive middle, positive small, positive zero,
zero, negative zero, negative small, negative middle,
negative big respectively. The Fuzzy domain of E, EE
and U as follows:


6, 5, 4, 3,2, 1, 0, 0, 1, 2, 3, 4, 5, 6E


(4)
()
PID
uk
()
Fuzzy
uk
()
A
uk
() ()
APID
uku k



(1) *()
Fuzzy
uk



dedt

Figure 1. Fuzzy-PID Control Diagram.

R. GUO ET AL.
8

6, 5, 4, 3, 2, 1,0, 1, 2, 3, 4, 5, 6EE         

(5)

6, 5, 4, 3, 2, 1,0, 1, 2, 3, 4, 5, 6U       (6)
The membership is shown in “Tables 1-3”.
3.1.2. Conclude the Control Rules
Based on the actual work conditions and expertise, after
tests again and again, the fuzzy control rules are con-
firmed as is shown in “Table 4”. When the error is nega-
tive, and the error ratio is also negative, the error is in an
increasing trend. Selecting PB or PM control amount is
to remove the existing erro rs. When the error is neg ative,
and the error ratio is positive, the error is in reducing
Table 1. Membership assignment of E.
Table 2. Membership assignment of EE.
Table 3. Membership assignment of U.
Table 4. Fuzzy control rules.
EC
E NB NMNS O PS PMPB
NB PB PB PB PB PM PS O
NM PB PB PB PM PS O O
NS PB PB PMPS O NS NS
NO PMPS PS O NS NMNM
PO PMPMPS O NS NS NM
PS PS PS O NS NM NB NB
PM O O NS NM NB NB NB
PB O NS NM NB NB NB NB
trend. It is necessary to take smaller control amount to
eliminate the error as soon as possible and prevent the
occurrence of overshoot. When the error is positive, and
the error ratio is negative, the error is in the decreasing
trend. It is advisable to take smaller control amount.
When the error is positiv e, and the error ratio is positive,
the error is in increasing trend, selecting the PB or PM.
Computer language presentation of the above rules
are:
if E=NB and EE=NB then, U=PB (7)
if E=NB and EE=NM then, U=PB (8)
if E=NB and EE=NS then, U=PB (9)
if E=NB and EE=O then, U=PB (10)
The control rules can be concluded as follows:
From formula 7 can achieve such fuzzy expression:

1
T
E
EE U
RNBNB PB  (11)
In the same way, the fuzzy relation of 1256
can be obtained. From the above 56 Fuzzy relation ma-
trix can reach to total Fu zzy relation matrix.
,RRR
56
1i i
R

R (12)
Formula 12 represents the overall control decision
fuzzy relation matrix, including controller input and out-
put relation. With the relation, the fuzzy amount of out-
put U can get from E, EE.


UUE EER  (13)
The Assignment is shown in “Table 5”.
3.1.3. De fuzzification
Applying the maximum membership principle to the
output value defuzzification processing in fuzzy control
table. Selecting the maximum element U* o f me mb er s hi p
functio n as t he final outp ut value, u(k)=U*.

R. GUO ET AL. 9
3.2. Confirmation of Coregulatory Factor α
The Confirmation of coregulatory factor

is based on
the deviation of speed setting and actual rotor speed
feedback. When the deviation is small, the stress is put
on PID adjustment, selecting larger value of

to
compensate for the Fuzzy Control which can not elimi-
nate the static error; when the deviation is large, empha-
sis is put on the Fuzzy Control, selecting larger value of

to speed up the system response.
Fuzzy PID controller total output is:
()() (1)*()
A PIDFuzzy
uku kuk

   (14)
4. Simulation Test Platform
To test the effectiveness of control strategy, the tests are
carried out in Simulation Test Platform, adopting the
program shown in “Figure 2”.
Bladed software is a kind of wind turbine simulation
software developed by Garrad Hassan Company, and is
the authority of wind turbine design, manufacture and
certification. Bladed has been widely used in the interna-
tional wind power industry, whose software simulation
curve has been compared with over 20 series of wind
turbine real working curve and prove to be pr ovid ed with
high simulation accuracy[4].
Table 5. Assignment table of the fuzzy control rules.
Table 6. Coordination factor.
Figure 2. Block diagram of the simulation test platform.
The test platform employs Bladed software for wind
turbine model. Through external controller function pro-
vided by Bladed software, to write DLL files in accor-
dance with its variable definitions address to achieve data
exchange of Bladed and external controller.
Beckhoff PLC offers ADS communication function.
Through the ADS functions call, the user can achieve
customers-service communications between ADS equip-
ment and PLC tasks to complete the exchange of data
between the peripheral unit and PLC.
To achieve real-time data exchange between the
Bladed (wind turbine model) and PLC controller (control
algorithm), calling DLL file at 20 ms in Bladed and ex-
ternal controller function block. Through ADS commu-
nication reading PLC wind turbine data, HMI monitors
the interface and displays the data.
5. Simulation Result
Imposing a set of step wind and a set of full-wave wind
to simulate wind speed change working condition to ver-
ify the rapidity and stability of control algorithm under
the circumstance of wind speed change; Simulating low
voltage grid failure condition at the 30 s voltage drop
process to verify low voltage ride through (LVRT) per-
formance of control algorithm. HMI interface records the
wind speed in the whole process (shown in cyan line),
pitch angle (in red line), generator speed (in blue line),
torque (in yellow line), power curve (in green line), as
shown in “Figure s 3-5”.
Figure 3. Wind turbine control curve under step wind.
Figure 4. Wind turbine control curve under full-wave wind.

R. GUO ET AL.
10
and gradually stabilize the rotor speed at the rated speed.
In this process, wind turbine rotor speed has been well
controlled, without speeding or off-grid, and also can be
quickly restored to a stable state.
6. Conclusions
The paper puts forward an improved pitch control algo-
rithm based upon Fuzzy-PID Control. The method avoids
the disadvantages of the traditional simple PID Control
such as great inertia, pure lag and non-linearity provided
with wind turbine. In the complex and rapidly changing
working conditions, the new type controller can effec-
tively improve the static and dynamic performance of the
wind turbine; at the same time avoid the wind turbine
speeding, and the occurrence off-grid.
Figure 5. Wind turbine control curve under low voltage
fault.
“Figure 3” is control process applying a set of step
wind to wind turbine model: ① wind speed step is from
12 m / s to 16 m / s; ② wind speed step is from 16 m / s
to 20 m / s; ③ wind speed step is from 20 m / s step to
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“Figure 4” is control process imposing a set of
full-wave to wind turbine model：① Initial wind speed is
14 m/s, amplitude is 4 m/s, and period is 30 s；② initial
wind speed is 14 m/s, amplitude is 6 m/s, and period is
25 s.
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“Figure 5” is no torque output (low voltage fault)
control process applied to the wind turbine model with
duration of 30 s. Simply to test the effectiveness of the
algorithm, the low voltage fault process is simplified
from torque sudden drops to sudden uplift.
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Observed from the above simulation curves, with sud-
den wind speed increase or decrease, the pitch angle can
change quickly. When the speed deviation or deviation
ratio is relatively great, pitch angle curve slope is large,
Fuzzy Control plays a major role, and greatly improves
the fast performance of wind turbine; When the speed
deviation or deviation ratio is relatively small, the pitch
angle slope curve is small, PID Control plays a major
role to gradually eliminate static error and improve con-
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Under low voltage fault conditions, the wind turbine
torqu e suddenly drop , the rotor sp eed incre ases, the p itch
system angle increase quickly to inhibit wind turbine
rotor speed from increasing, and gradually stabilize the
rotor speed at the rated speed; With failure recovery and
torque restore, rotor speed declines. Pitch system angle
decrease quickly to restrain rotor speed from decreasing,
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