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FUZZY LOGIC CONTROL
FOR HYBRID ENERGY
SYSTEM
Fuzzy logic controller (FLC) for
Photovoltaic-Wind-Battery (PVWB)
hybrid syst...
CONTENTS
• Introduction
• Types of hybrid energy system
• Modeling of system
• Fuzzy Logic Controller
-Fuzzification of sy...
Introduction
• Hybrid energy system includes several (two or more) energy
sources with appropriate energy conversion techn...
Types of Hybrid Energy
Systems
Photovoltaic -Diesel-Wind hybrid system Photovoltaic-Wind hybrid system
Any combination of ...
Modeling of Hybrid
System components
• Various modelling techniques have been
developed by researchers to model components...
PV Mathematical model
• A PV system consists of many cells which
connected in series and parallel to provide the
desired o...
WIND TURBINE GENERATOR
MODEL
Typical power curve of wind turbine of capacity 3KW
WIND TURBINE GENERATOR
MODEL
• The power curve of a wind turbine is non-linear
• The data is available from the manufactur...
THE BATTERY STORAGE
MODEL
• The battery model describes the relationship
between the voltage, current and the state of
cha...
FUZZY LOGIC CONTROLLER
• The fuzzy controller makes a decision based on a number of learned
or predefined rules, rather th...
Power management of PV wind
hybrid system.
FUZZIFICATION
•The reference load is compared with the generated power to
produce the error signal which used as input sig...
FUZZIFICATION
• Membership function values are assigned to the linguistic
variables, using seven fuzzy subsets:
NB (negati...
FUZZIFICATION
Error membership functions Variation of error membership functions
Output space.
FUZZY INFERENCE SYSTEM
(FIS)
• The composition operation is the method by
which the controlled output is generated.
• The ...
RULE BASE
OUTPUT OF FLC
Control surface of the designed FLC.
DEFUZZIFICATION
• As a system usually requires a non fuzzy value of control, a
defuzzification stage is needed
• Defuzzifi...
SYSTEM SIMULATION
SIMULINK block diagram of FLC.
SYSTEM SIMULATION
Electrical sub-system using FLC control
CONVENTIONAL PI
CONTROLLER
Proportional-integral (PI) controllers are the most commonly used controllers,
especially in th...
RESULTS AND DISCUSSIONS
Responses of the load
power using the FLC and
the PI controller.
RESULTS AND DISCUSSIONS
Responses of the load
power using the FLC
and the PI controller at
sudden variations in
insolation...
RESULTS AND DISCUSSIONS
PI
The voltage on the load
reaches a value of 380 V at 2
s with the PI controller.
The maximum ove...
CONCLUSION
• Design via simulation allows studying different options, considering
various influence parameters and effecti...
CONCLUSION
• When there is no wind or its cloudy, the loads are supplied only
with batteries. When the batteries are empty...
REFERENCES
[1] Abd El-Shafy A. Nafeh Fuzzy Logic Operation Control for PV-Diesel-Battery Hybrid
Energy System- The Open Re...
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Fuzzy Logic Control of Hybrid Energy System

  1. 1. FUZZY LOGIC CONTROL FOR HYBRID ENERGY SYSTEM Fuzzy logic controller (FLC) for Photovoltaic-Wind-Battery (PVWB) hybrid system A Presentation by Suraj K (1RV10EE053)
  2. 2. CONTENTS • Introduction • Types of hybrid energy system • Modeling of system • Fuzzy Logic Controller -Fuzzification of system -Fuzzy Inference Engine -Defuzzificaton • Results and Discussions • Conclusion
  3. 3. Introduction • Hybrid energy system includes several (two or more) energy sources with appropriate energy conversion technology connected together to feed power to local load/grid. Estimated Potential in India Source-NIT Calicut
  4. 4. Types of Hybrid Energy Systems Photovoltaic -Diesel-Wind hybrid system Photovoltaic-Wind hybrid system Any combination of these energy sources together can form a hybrid energy system
  5. 5. Modeling of Hybrid System components • Various modelling techniques have been developed by researchers to model components of hybrid renewable energy system (HRES) • Performance of individual components is either modelled by deterministic or probabilistic approaches. • General methodology for modelling HRES components like PV, wind and battery is described as follows.
  6. 6. PV Mathematical model • A PV system consists of many cells which connected in series and parallel to provide the desired output terminal voltage and current, and exhibits a nonlinear I–V characteristic
  7. 7. WIND TURBINE GENERATOR MODEL Typical power curve of wind turbine of capacity 3KW
  8. 8. WIND TURBINE GENERATOR MODEL • The power curve of a wind turbine is non-linear • The data is available from the manufacturer and can be easily digitized and the resulting table can be used to simulate the wind turbine performance • The outlet energy of a turbine could be calculated from its power-speed curve Where, Vs Velocity (m/s). Vi Cut in velocity (m/s). Vr Rated velocity (m/s). Vo Cut out velocity (m/s).
  9. 9. THE BATTERY STORAGE MODEL • The battery model describes the relationship between the voltage, current and the state of charge(SOC) Where, VB Battery terminal voltage (V). IB Battery current (A) (positive when charging and negative when discharging). Vr Rest voltage (V). RB Internal resistance of the battery (ohms).
  10. 10. FUZZY LOGIC CONTROLLER • The fuzzy controller makes a decision based on a number of learned or predefined rules, rather than numerical calculations. Block diagram of fuzzy logic control for PV wind battery hybrid system.
  11. 11. Power management of PV wind hybrid system.
  12. 12. FUZZIFICATION •The reference load is compared with the generated power to produce the error signal which used as input signal to FLC •The value of input error (e) and change of error (ce) are normalized by an input scaling factor.
  13. 13. FUZZIFICATION • Membership function values are assigned to the linguistic variables, using seven fuzzy subsets: NB (negative big), NM (negative medium), NS (negative small), ZE (zero), PS (positive small), PM (positive medium), and PB (positive big). • The triangular shape of the membership function of this arrangement presumes that for any particular input there is only one dominant fuzzy subset
  14. 14. FUZZIFICATION Error membership functions Variation of error membership functions Output space.
  15. 15. FUZZY INFERENCE SYSTEM (FIS) • The composition operation is the method by which the controlled output is generated. • The Max–Min method is used. • The output membership function of each rule is given by the Minimum • Thus a total of 49 rules are formed which define the output • The rule base of the FLC is as shown
  16. 16. RULE BASE
  17. 17. OUTPUT OF FLC Control surface of the designed FLC.
  18. 18. DEFUZZIFICATION • As a system usually requires a non fuzzy value of control, a defuzzification stage is needed • Defuzzification method used in this system is the center of gravity method which is simple and fast • Center of gravity method consists of finding the centroid of the area bounded by the controller output MF and its abscissa is taken as the crisp controlling value • The mathematical expression of the centre of gravity method is shown.
  19. 19. SYSTEM SIMULATION SIMULINK block diagram of FLC.
  20. 20. SYSTEM SIMULATION Electrical sub-system using FLC control
  21. 21. CONVENTIONAL PI CONTROLLER Proportional-integral (PI) controllers are the most commonly used controllers, especially in the electronics industry.
  22. 22. RESULTS AND DISCUSSIONS Responses of the load power using the FLC and the PI controller.
  23. 23. RESULTS AND DISCUSSIONS Responses of the load power using the FLC and the PI controller at sudden variations in insolation. The simulation is carried out for a daily peak load of 5500 W and at insolation level of 1000 W/m2
  24. 24. RESULTS AND DISCUSSIONS PI The voltage on the load reaches a value of 380 V at 2 s with the PI controller. The maximum overshoot voltage that can be reached is 392 V FLC The voltage on the load reaches a value of 380 V at 0.05 s with the FLC. The maximum overshoot value is 384 V
  25. 25. CONCLUSION • Design via simulation allows studying different options, considering various influence parameters and effectively fulfils the system/user requirements. • The FLC is, mainly, designed to overcome the nonlinearity and the associated parameters variation of the components therefore yielding better system response at both transient and steady state conditions. • When the produced energy is greater and the loads are low, the wind turbine and PV cells must be arranged to recharge the batteries. This can be done by the management of the energy.
  26. 26. CONCLUSION • When there is no wind or its cloudy, the loads are supplied only with batteries. When the batteries are empty, the loads will have no energy supply. To prevent this situation, a diesel generator can be added to the system or the system can be supplied with energy by the main network. • The obtained simulation results indicate that the response of the load power in case of using the FLC is better and faster than that obtained in case of using the PI controller at all atmospheric conditions • FLC has two input signals which are error and change of error. 49 rule bases, the COG and Max-Min method were used
  27. 27. REFERENCES [1] Abd El-Shafy A. Nafeh Fuzzy Logic Operation Control for PV-Diesel-Battery Hybrid Energy System- The Open Renewable Energy Journal, 2009, 2, 70-78 [2]Onur ¨Ozdal MENG,∗, ˙Ismail Hakkı ALTAS,Fuzzy logic control for a wind/battery renewable energy production system -Turk J Elec Eng & Comp Sci, Vol.20, No.2, 2012 [3]Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah- Modeling and Control PV-Wind Hybrid System Based On Fuzzy Logic Control Technique ISSN: 1693-6930 [4]M.R. Patel, Wind and Solar Power Systems, Boca Raton, Florida, CRC Press, 2006. [5]V. Rao, C. Chinnagounder, “Analysis of hybrid power system”, First Asia International Conference on Modelling and Simulation, pp. 48-52, 2007. [6]Moseley, P.T. Energy storage in Remote Area Power Supply (RAPS) systems. J. Power Sources, 2006, 155, 83-87. [7]Lee, C.C. Fuzzy logic in control systems: fuzzy logic controller-Part I. IEEE Trans. Syst. Man Cybernet., 1990, 20(2), 404-435 [8]H. Weiss, J. Xiao, “Fuzzy system control for combined wind and solar power distributed generation unit”, IEEE International Conference on Industrial Technology, Vol. 2, pp. 1160-1165, 2003.
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Fuzzy logic control of a hybrid wind-battery system

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