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# Algiers the 30th October 2013, Tassili, Hilton Hotel.

## Présentation au sujet: "Algiers the 30th October 2013, Tassili, Hilton Hotel."— Transcription de la présentation:

Algiers the 30th October 2013, Tassili, Hilton Hotel.
3th International Conference on Systems and Control (ICSC’13) 29-31 October 2013, Hotel Hilton, Algiers, Algeria Title: Fuzzy Logic Control Algorithm of Grid Connected Doubly Fed Induction Generator driven by Vertical Axis Wind Turbine in Variable Speed Presented by: Ridha CHEIKH Researcher at Solar Equipments Development Unit, UDES, EPST/CDER Algiers the 30th October 2013, Tassili, Hilton Hotel.

Presentation Outline INTRODUCTION WEC-System Modeling
Proposed Control strategy Fuzzy logic controller design Simulation results conclusion

Introduction 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.

System description: 3 Kw (average power) a Savonuis Vertical Axis Wind Turbine. Residential application (buildings, Street Lighting…etc) WECSystem Main Parts : Wind

System Modeling 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:

System Modeling The DFIM Complex model: Current-Flux equations:
Electromagnetic Torque expression: Active & Reactive powers expression:

Oriented flux strategy :
Control Strategy Oriented flux strategy : If the virtual grid flux vector is aligned on the d axis it is found that: d-axis s w Rotor-ref-axis And after calculation : q-axis w q s q Stator-ref-axis 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.
Control Strategy General block diagram of DFIG control scheme.

Fuzzy logic controller design
fuzzy Controller Inner structure:

Fuzzification Méthode:
All of the Fuzzy controller input and output have a Triangular form Membership fonctions (easy in calculation) Inference Engine: Is a Mamdani-type and based on the following rules table. Rules Table : e de BN MN SN VSN ZE VSP SP MP BP NB NM NS PVS PS PM PB (P, N)=(Positive, Negative), (B, M, S,ZE) =(Big, Medium, Small, Zero) V=(Very).

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.. Optimization of the input/output of the fuzzy controller: Optimization Algorithm bloc 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-).

Maximum Power Point Tracking Strategy
Several techniques can be used such as the gradient method, the estimate method…etc. In our case: Extracting the maximum wind power through the Savonuis wind turbine needs: Operating at variable speed + well-known of the Savonuis wind turbine aerofoil.

Simulation Results Wind Speed (m/s) Stator Active Power Response

Stator Reactive Power Response
Stator Power factor Cp of the Vertical Axis Wind Turbine Rotor Current (A)

Conclusion 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. Maximum Power Point Tracking Strategy is included basing on the turbine aerofoil. Simulation tests have been done where they have shown the stability and robustness of the system .

Vertical Axis Wind Turbine
Appendix (System data) Parameters Values DFIG Output power Pn/kW 7.5 Stator resistance Rs/Ω 0.455 Rotor resistance Rr/Ω 0.62 Stator inductance Ls /H 0.084 Rotor inductance Lr /H 0.081 Mutual inductance Msr/H 0.078 Number of pair poles 2 Inertia moment J/(N·m·s2) 0.3125 Rubbing factor F 6.73e-3 Vertical Axis Wind Turbine Rated power KW 7 Density of air (ρ) kg/m3 1.2 Area swept (Diameter×height) m2 40 Rotor diameter m 4 Optimal coefficient Cpmax 1.9 Gearbox ratio

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