Paper Code: 134

A Review of Maximum Power Point Tracking Algorithms for Photovoltaic Systems under Uniform and Non-Uniform irradiances

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A Review of Maximum Power Point Tracking Algorithms for Photovoltaic

Systems under Uniform and Non-Uniform Irradiances

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byT.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

IntroductionWhy Solar PV power systems? maintenance free noise free environmental friendly

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Block Diagram of PV Generation Systems

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Modelling of PV module Two diode model with seven parameters Two diode model with five parameters

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Two Diode model of the PV cell

Two Diode model of the PV cell

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simplified

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Series Parallel combination of the PV array

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Characteristics of PV Cell under PSC

Operation of PV array under partial shading

PV curves for each string

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PV curve of the entire array

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CONVENTIONAL MPPT TECHNIQUES

Perturb and Observe Hill Climbing algorithm Incremental Conductance short circuit current open circuit voltage and ripple correlation control approaches

Intelligent MPPT Techniques

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FLC based MPPTMamdanis 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

Artificial Neural Network based MPPT

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It is suitable for the system that can get sufficient training data

PSO based MPPT

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Demerits:• It is incapable of searching maxima• local convergence accuracy is not

high

ACO based MPPT

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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

Firefly algorithmFirefly 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 unisex2) Brightness of the firefly is determined from

the encoded objective function3) 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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Firefly alorithmThe algorithm can be summarized as

follows:

I. Generate a random solution set, {x1, x2, . . . xk}

II. Compute intensity for each solution member,{I1, I2, ……. Ik}

III.Move each firefly i towards other brighter fireflies and if there is no other brighter firefly, move it randomly

IV. Update the solution setV. Terminate if a termination criterion is

fulfilled other wise go back to stepII. 04/12/2023 18

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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References. .

1. Bidyadhar Subudhi and Raseswari Pradhan (2013) “A Com-parative Study on Maximum Power Point Tracking Tech-niques for Photovoltaic Power Systems” IEEE Transactions on Sustainable Energy,Vol. 4, No. 1

2. Moacyr Aureliano Gomes de Brito, Luigi Galotto,(2013) Jr., Leonardo Poltronieri Sampaio, Guilherme de Azevedo e Melo, and Carlos Alberto Canesin, Senior Member, “Evalua-tion of the Main MPPT Techniques for Photovoltaic Applica-tions” IEEE Transactions on Industrial Electronics, VOL. 60, NO. 3.

3. M. A. S. Masoum, H. Dehbonei, and E. F. Fuchs, 2002 “Theo-retical and experimental analyses of photovoltaic systems with voltage and current based maximum power point track-ing,” IEEE Trans. Energy Conv., vol. 17, no. 4, pp. 514–522

4. B. Subudhi and R. Pradhan, 2011 “Characteristics evalua-tion and parameter extraction of a solar array based on ex-perimental analysis,” in Proc. 9th IEEE Power Electron. Drives Syst., Singapore

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References

5. [T. Esram, J. W. Kimball, P. T. Krein, P. L. Chapman, and P. Midya, (2006) “Dynamic maximum power point tracking of photovoltaic arrays using ripple correlation control,” IEEE Trans. Power Electron., vol. 21, no.5, pp. 1282–1291

6. K. Ishaque, Z. Salam, and H. Taheri,(2011) “Simple, fast and accurate two diode model for photovoltaic modules,” Solar Energy Mater. Solar Cells, vol. 95, pp. 586–594

7. K. Ishaque, Z. Salam, and H. Taheri,(2011) “Accurate MATLAB simulink PV system simulator based on a two-diode model,” J. Power Electron., vol. 11, pp. 179–187

8. Kashif Ishaque, Zainal Salam, Muhammad Amjad, and Saad Mekhilef,( 2012) “An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation” IEEE transactions on Power Eelectronics, vol. 27, no. 8

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References

9. MASAFUMI MIYATAKE, MUMMADI VEERACHARY, NOBUHIKO FUJII, HIDEYOSHI KO , (2011) “ Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A PSO Approach” IEEE Transactions on Aerospace and Electronic Systems VOL. 47, NO. 1.

10.Yi-Hwa Liu, Shyh-Ching Huang, Jia-Wei Huang, and Wen-Cheng Liang (2012) “A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions” IEEE Transactions on Energy Conversion, VOL. 27, NO. 4.

11.Mahmoud A. YOUNIS , Tamer KHATIB, Mushtaq NAJEEB, A Mohd ARIFFIN (2012), An Improved Maximum Power Point Tracking Controller for PV Systems Using Artificial Neural Network Przegląd Elektrotechniczny, R. 88 NR 3b

12.Whei-Min Lin, Member, IEEE, Chih-Ming Hong, and Chiung-Hsing Chen 2011, “Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System” IEEE Transactions on Power Electronics, VOL. 26, NO. 12.

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13. Lian Lian Jiang, Douglas L. Maskell, Jagdish C. Patra (2013), A Novel Ant Colony optimization based maximum power point tracking for photovoltaic systems under partially shaded con-ditions, Energy and Buildings Vol 58. Pgs 227-236.

14. N. Chai-ead, P. Aungkulanon, and P. Luangpaiboon, Member, IAENG (2011), Bees and Firefly Algorithms for Noisy Non-Linear Optimization Problems Proceedings of the 26th Inter-national multi Conference of engineers andComputer Scien-tistsVolume II.

15. Surafel Luleseged Tilahun and Hong Choon Ong(2012) “Modi-fied Firefly Algorithm” Hindawi Publishing Corporation, Jour-nal of Applied Mathematics, Article ID 467631, 12 pages

16. A. Mathew and A. I. Selvakumar, (2006) “New MPPT for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive network,” IEEE Trans. Energy Conv., vol. 21, no. 3, pp. 793–803.

17. C.-S. Chiu, (2010)“T-S fuzzy maximum power point tracking control of solar power generation systems,” IEEE Trans. En-ergy Conv., vol. 25, no. 4, pp. 1123–1132.

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THANK YOU

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