1. Paper Code: 134
A Review of Maximum Power Point Tracking
Algorithms for Photovoltaic Systems under
Uniform and Non-Uniform irradiances
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2. by
T.LOGESWARAN
Assistant Professor(SRG)
Department of EEE
Kongu Engineering College,
Perundurai, Erode, Tamilnadu, India
Dr.A.SENTHIL KUMAR, M.E.,PhD.,
Prof & Head, Department of EEE
Dr.Mahalingam College of Engineering and Technology,
Pollachi, Tamilnadu, India
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3. Introduction
Why Solar PV power systems?
 maintenance free
 noise free
 environmental friendly
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4. Block Diagram of PV Generation Systems
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5. Modelling of PV module
 Two diode model with seven parameters
 Two diode model with five parameters
Two Diode model of the PV cell
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6. Two Diode model of the PV cell
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7. simplified
Series Parallel combination of the PV array
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8. Characteristics of PV Cell under PSC
Operation of PV array under partial shading
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9. PV curves for each string
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10. PV curve of the entire array
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11. CONVENTIONAL MPPT TECHNIQUES
 Perturb and Observe





Hill Climbing algorithm
Incremental Conductance
short circuit current
open circuit voltage and
ripple correlation control approaches
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12. Intelligent MPPT Techniques
FLC based MPPT
 Mamdanis method is used for fuzzy inference
 Centre of gravity method for defuzzification
and the duty ratio is computed
 fuzzy rule base, which is dependent on the
experience of algorithm developers –
influences on the performance of MPPT
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13. Artificial Neural Network
based MPPT
It is suitable for the system that can get sufficient training
data
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14. PSO based MPPT
Demerits:
• It is incapable of searching maxima
• local convergence accuracy is not high
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15. ACO based MPPT
The number of ants (M), Convergence speed (€), Solution
archive (K) and Locality of search process (Q) ---- these
parameters are to decided by the user
When choosing the number of ants, there will be tradeoff
between convergence speed and tracking accuracy
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16. Firefly algorithm
 Firefly algorithm is a bio inspired metaheuristic
algorithm for optimization problems
 Introduced in 2009 at Cambridge University by Yang
Rules for constructing firefly algorithm:
1) All fireflies are unisex
2) Brightness of the firefly is determined from the
encoded objective function
3) Attractiveness is directly proportional to brightness
but decreases with distance and a firefly will move
towards the brighter one and if there is no brighter
one, it will move randomly.
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17. 12/10/2013
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18. Firefly alorithm
The algorithm can be summarized as follows:
I.
II.
III.
IV.
V.
Generate a random solution set, {x1, x2, . . . xk}
Compute intensity for each solution
member,{I1, I2, ……. Ik}
Move each firefly i towards other brighter
fireflies and if there is no other brighter firefly,
move it randomly
Update the solution set
Terminate if a termination criterion is fulfilled
other wise go back to stepII.
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19. Conclusion:
 Conventional MPPT algorithms does not find the
global maxima. Intelligent MPPT techniques have
merits and demerits. It is concluded that FA based
MPPT will be suitable for partial shaded conditions.
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20. References. .
1.
2.
3.
4.
Bidyadhar Subudhi and Raseswari Pradhan (2013) “A Comparative
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21. References
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22. References
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24. THANK YOU
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