ALGERIA & RENEWABLE ENERGY (IRENA STATISTICS in 2009):

Renewables Electrical Supply: 3.6 PJ (0.2 % of the total supply)

Renewables generation: 342 GWh (0.8 % of the total Electricity generation)

Renewables capacity: 280 MW (3.4 %).

POTENTIAL :

Wind energy is one of the most rapidly growing sources of electricity all over the world. Thus It is predicted that 12% of the total world electricity demands will be supplied from wind energy by 2020.

Producing a balanced electrical power facing to the non regularity of the wind is still the main challenge in WECS.

Advanced control Techniques is a major player in Wind Power industry development.

3 Kw (average power) a Savonuis Vertical Axis Wind Turbine. Residential application (buildings, Street Lighting…etc)

Wind

System description:

WECSystem Main Parts :

Wind Speed & Wind turbine Modeling:

V(t)=6+0.2*sin(0.1047*t)+2*sin(0.2665*t)+sin(1.2930*t)+0.2*sin(3.6645*t)

The extracted power from the wind by the Savonuis wind turbine is:

The DFIM Complex model:

Current-Flux equations:

Active & Reactive powers expression:

Electromagnetic Torque expression:

Oriented flux strategy :

q-axisq-axis

su

s

Rotor-ref-axis

Rotor-ref-axis

d-axis

d-axis

Stator-ref-axisStator-ref-axis

s

ru

If the virtual grid flux vector is aligned on the d axis it is found that:

And after calculation :

So,

The above equations are coupled between themselves, so the coupling terms are considered as disturbances to be removed by the control, for this purpose, an optimized fuzzy logic controller is proposed in order to control the stator powers flow with desired performance. This type of controllers is chosen due to its competence in control and its implementation simplicity.

Due to the very fast-growing information technology, industry has already developed and released a few good design packages which can be successfully applied in different applications for a fuzzy controller design. Among them are: RT/Fuzzy Toolbox for MATRIXxTM by Integrated Systems Inc., Fuzzy Logic Toolbox for MATLABTM by The MathWorks Inc.,…etc.

Fuzzy Logic Toolbox for MATLABTM is chosen to design our Fuzzy controller.

General block diagram of DFIG control scheme.

fuzzy Controller Inner structure:

Fuzzification Méthode:

All of the Fuzzy controller input and output have a Triangular form Membership fonctions (easy in calculation)

Rules Table :

e

deBN MN SN VSN ZE VSP SP MP BP

BN NB NB NB NB NB NM NS PVS ZE

MN NB NB NB NB NM NS PVS ZE PVS

SN NB NB NB NM NS PVS ZE PVS PS

VSN NB NB NM NS PVS ZE PVS PS PM

ZE NB NM NS PVS ZE PVS PS PM PB

VSP NM NS PVS ZE PVS PS PM PB PB

SP NS PVS ZE PVS PS PM PB PB PB

MP PVS ZE PVS PS PM PB PB PB PB

BP ZE PVS PS PM PB PB PB PB PB

(P, N)=(Positive, Negative), (B, M, S,ZE) =(Big, Medium, Small, Zero) V=(Very).

Inference Engine: Is a Mamdani-type and based on the following rules table.

These rules were wisely chosen according to prescribe specifications, taking into account the system stability and performances, thus are represented by the overshoot, rise time and the settle time of the fuzzy system response.

Defuzification Method: 

Centre-of-area/gravity method is used in the defuzification procedure. even though it is very expensive in terms of calculation time, but it gives good results..

A right conversion of the actual measured value and the fuzzy value of such kinds are acceptable in the fuzzy space (for the error and its derivative) and in real space (for the control -output-).

Optimization of the input/output of the fuzzy controller:

Optimization Algorithm bloc

Extracting the maximum wind power through the Savonuis wind turbine needs: Operating at variable speed +

well-known of the Savonuis wind turbine aerofoil.

Maximum Power Point Tracking StrategySeveral techniques can be used such as the gradient method, the estimate method…etc.

In our case:

0 2 4 6 8 10 12

x 104

6

6.5

7

7.5

8

8.5

9

9.5

Time (s)

Win

d Sp

eed

(m/s

)

0 2 4 6 8 10 12

x 104

-4500

-4000

-3500

-3000

-2500

-2000

-1500

-1000

-500

0

Time (s)

Stat

or A

ctiv

e Po

wer R

espo

nse

(Wat

)5.5 5.5005 5.501 5.5015 5.502 5.5025 5.503 5.5035 5.504 5.5045 5.505

x 104

-4600

-4590

-4580

-4570

-4560

-4550

-4540

-4530

-4520

-4510

-4500

Zoom

Wind Speed (m/s)

Stator Active Power Response

0 2 4 6 8 10 12

x 104

-3000

-2000

-1000

0

1000

2000

3000

Time (s)

Stat

or R

eact

ive P

ower

(VAR

)

0 2 4 6 8 10 12

x 104

-1

-0.8

-0.6

-0.4

-0.2

0

Time (s)

Sta

tor

Pow

er F

acto

r

0 2 4 6 8 10 12

x 104

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Time (s)

Cp

of t

he V

ertic

al A

xis

Win

d T

urbi

ne

Stator Reactive Power Response Stator Power factor

Cp of the Vertical Axis Wind Turbine

0 2 4 6 8 10 12

x 104

-25

-20

-15

-10

-5

0

5

10

15

20

25

Time(s)

Roto

r Cur

rent

(A)

Rotor Current (A)

Point out on the Renewable energy, (Wind) in Algeria (IRENA Statestics).

3 Kw Residential Wind Energy Conversion System based on Vertical Axis Wind Turbine and Grid connected DFIG is proposed and modeled.

Oriented Grid Flux Strategy has been investigated to remove the complexity issue of the WECSystem.

Fuzzy Logic control algorithm is proposed using to control the stator powers flow of the Grid-connected DFIG following to specifications.

Simulation tests have been done where they have shown the stability and robustness of the system .

Maximum Power Point Tracking Strategy is included basing on the turbine aerofoil.

Parameters ValuesDFIG

Output power Pn/kW 7.5Stator resistance Rs/Ω 0.455Rotor resistance Rr/Ω 0.62

Stator inductance Ls /H 0.084Rotor inductance Lr /H 0.081

Mutual inductance Msr/H 0.078Number of pair poles 2

Inertia moment J/(N·m·s2) 0.3125Rubbing factor F 6.73e-3

Vertical Axis Wind TurbineRated power KW 7

Density of air (ρ) kg/m3 1.2Area swept (Diameter×height) m2 40

Rotor diameter m 4Optimal coefficient Cpmax 1.9

Gearbox ratio 40

Appendix (System data)