This document reviews maximum power point tracking (MPPT) algorithms for photovoltaic systems. It discusses conventional MPPT techniques like perturb and observe and incremental conductance. It also examines intelligent MPPT techniques using fuzzy logic, artificial neural networks, particle swarm optimization, ant colony optimization, and firefly algorithms. The firefly algorithm is proposed as suitable for partial shaded conditions due to its ability to find the global maximum power point. The document concludes that while conventional MPPT methods have limitations, intelligent techniques have merits and drawbacks, and further research is needed.

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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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.
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21. References
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22. References
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24. THANK YOU
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