A COMPREHENSIVE OVERVIEW OF SOFT COMPUTING BASED MPPT TECHNIQUES FOR PARTIAL SHADING CONDITIONS IN PV SYSTEMS

Abstract

Nowadays, solar or photovoltaic energy is the most commonly used renewable energy resources in the world. Despite its advantages such as freely available, low maintenance cost, pollution-free, inexhaustible, and reliable, its low conversion efficiency is a major drawback. To increase the efficiency of the photovoltaic system, all photovoltaic modules in the array must be operated at maximum power point. Therefore, maximum power point tracking technique is used for predicting and tracking the maximum power point. In the literature, maximum power point tracking techniques are generally classified as soft computing and conventional. Soft computing techniques are more preferred from both of them, because they can accurately track maximum power point of photovoltaic systems. In this study, an extensive review of soft computing based maximum power point tracking techniques under partial shading conditions until today is presented. The techniques are compared from the point of photovoltaic array dependency, sensors required, tracking efficiency, tracking speed, algorithm complexity, and oscillation around maximum power point.

Keywords

• Photovoltaic System,Maximum Power Point Tracking,Soft Computing Technique

PV SİSTEMLERDE KISMİ GÖLGELENME KOŞULLARINDA ESNEK HESAPLAMA TABANLI MAKSİMUM GÜÇ NOKTASI İZLEME TEKNİKLERİNİN KARŞILAŞTIRILMASI

Abstract

Günümüzde güneş ya da fotovoltaik enerji yenilenebilir enerji kaynakları arasında yaygın olarak kullanılmaktadır. Güneş enerjisi, maliyetsiz, atmosfer dostu,  işletme ve bakım maliyetinin az olması ve evrensel olarak her yerde bulunması gibi avantajlarına rağmen, düşük enerji verimliliği en büyük dezavantajıdır. Fotovoltaik sistemlerin verimliliğini arttırabilmek için fotovoltaik dizideki modüller maksimum güç noktalarında çalıştırılmalıdır. Bu nedenle, maksimum güç noktasını tahmin etmek ve izlemek için maksimum güç noktası izleme teknikleri kullanılır. Literatürde, maksimum güç noktası izleme teknikleri genellikle esnek hesaplama ve klasik teknikler olmak üzere sınıflandırılır. Ancak, maksimum güç noktasını doğru bir şekilde takip edebildikleri için esnek hesaplama teknikleri tercih edilmektedir. Bu çalışmada, geçmişten günümüze kadar kısmi gölgeleme koşullarında esnek hesaplama tabanlı maksimum güç noktası izleme tekniklerinin kapsamlı bir derlemesi sunulmuştur. Teknikler, fotovoltaik dizi bağımlılığı, gereken sensörler, takip verimliliği, takip hızı, algoritma karmaşıklığı ve maksimum güç noktası etrafında salınım durumları açısından karşılaştırılmıştır. 

Keywords

• Fotovoltaik Sistem,Maksimum Güç Noktası Takibi,Esnek Hesaplama Teknikleri

References

1. Ahmed, J., & Salam, Z., 2014. A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Applied Energy, 119, 118-130.
2. Ahmed, J., & Salam, Z., 2015. A critical evaluation on maximum power point tracking methods for partial shading in PV systems. Renewable and Sustainable Energy Reviews, 47, 933-953.
3. Amir, A., Selvaraj, J., & Rahim, N. A., 2016. Study of the MPP tracking algorithms: Focusing the numerical method techniques. Renewable and Sustainable Energy Reviews, 62, 350-371.
4. Babu, T. S., Rajasekar, N., & Sangeetha, K., 2015. Modified particle swarm optimization technique based maximum power point tracking for uniform and under partial shading condition. Applied soft computing, 34, 613-624.
5. Badis, A., Mansouri, M. N., & Sakly, A., 2016. PSO and GA-based maximum power point tracking for partially shaded photovoltaic systems. In Renewable Energy Congress (IREC), 2016 7th International (pp. 1-6). IEEE.
6. Bana, S., & Saini, R. P, 2017. Experimental investigation on power output of different photovoltaic array configurations under uniform and partial shading scenarios. Energy, 127, 438-453.
7. Bendib, B., Belmili, H., & Krim, F., 2015. A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems. Renewable and Sustainable Energy Reviews, 45, 637-648.
8. Belhachat, F., Larbes, C., 2015. Modeling, analysis and comparison of solar photovoltaic array configurations under partial shading conditions. Solar Energy, 120, 399-418.

9. Belhachat, F., & Larbes, C., 2017. Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions. Renewable and Sustainable Energy Reviews, 77, 875-889.
10. Bhatnagar, P., & Nema, R. K., 2013. Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews, 23, 224-241.
11. Bidram, A., Davoudi, A., & Balog, R. S., 2012. Control and circuit techniques to mitigate partial shading effects in photovoltaic arrays. IEEE Journal of Photovoltaics, 2(4), 532-546.
12. Bingöl, O., & Özkaya, B., 2018. Analysis and comparison of different PV array configurations under partial shading conditions. Solar Energy, 160, 336-343.
13. Bouilouta, A., Mellit, A., & Kalogirou, S. A., 2013. New MPPT method for stand-alone photovoltaic systems operating under partially shaded conditions. Energy, 55, 1172-1185.
14. Chu, S. C., & Tsai, P. W., 2007. Computational intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control, 3(1), 163-173.
15. Das, S. K., Verma, D., Nema, S., & Nema, R. K., 2017. Shading mitigation techniques: State-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews, 78, 369-390.
16. Dileep, G., & Singh, S. N., 2017. Application of soft computing techniques for maximum power point tracking of SPV system. Solar Energy, 141, 182-202.
17. Dhivya, P., & Kumar, K. R., 2017. MPPT based control of sepic converter using firefly algorithm for solar PV system under partial shaded conditions. In Innovations in Green Energy and Healthcare Technologies (IGEHT), 2017 International Conference on IEEE, 1-8.
18. Dorigo, M., & Gambardella, L. M., 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1), 53-66.
19. Eltawil, M. A., & Zhao, Z., 2013. MPPT techniques for photovoltaic applications. Renewable and Sustainable Energy Reviews, 25, 793-813.
20. Enany, M. A., Farahat, M. A., & Nasr, A., 2016. Modeling and evaluation of main maximum power point tracking algorithms for photovoltaics systems. Renewable and Sustainable Energy Reviews, 58, 1578-1586.
21. Gandomi, A. H., & Yang, X. S., 2014. Chaotic bat algorithm. Journal of Computational Science, 5(2), 224-232.
22. Guo, L., Meng, Z., Sun, Y., & Wang, L., 2016. Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm. Energy conversion and management, 108, 520-528.
23. Guo, L., Meng, Z., Sun, Y., & Wang, L., 2017. A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition. Energy.
24. Huang, C., Zhang, Z., Wang, L., Song, Z., & Long, H., 2017. A novel global maximum power point tracking method for PV system using Jaya algorithm, 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 1-5.
25. Ishaque, K., Salam, Z., Taheri, H., & Shamsudin, A., 2011. Maximum power point tracking for PV system under partial shading condition via particle swarm optimization. In Applied Power Electronics Colloquium (IAPEC), IEEE, 5-9.
26. Ishaque, K., Salam, Z., Amjad, M., & Mekhilef, S., 2012. An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE transactions on Power Electronics, 27(8), 3627-3638.
27. Ishaque, K., & Salam, Z., 2013. A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renewable and Sustainable Energy Reviews, 19, 475-488.
28. Jang, J. S., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
29. Ji, Y. H., Jung, D. Y., Kim, J. G., Kim, J. H., Lee, T. W., & Won, C. Y., 2011. A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions. IEEE Transactions on power electronics, 26(4), 1001-1009.
30. Jiang, L. L., Maskell, D. L., & Patra, J. C., 2013. A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings, 58, 227-236.
31. Jiang, L. L., & Maskell, D. L., 2014. A uniform implementation scheme for evolutionary optimization algorithms and the experimental implementation of an ACO based MPPT for PV systems under partial shading. In Computational Intelligence Applications in Smart Grid (CIASG), IEEE, 1-8.
32. Jordehi, A. R., 2016. Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches. Renewable and Sustainable Energy Reviews, 65, 1127-1138.
33. Jumpasri, N., Pinsuntia, K., Woranetsuttikul, K., Nilsakorn, T., & Khan-ngern, W., 2014. Improved particle swarm optimization algorithm using average model on MPPT for partial shading in PV array. In Electrical Engineering Congress (iEECON), 2014 International IEEE, 1-4.
34. Kaced, K., Larbes, C., Ramzan, N., Bounabi, M., & Elabadine Dahmane, Z., 2017. Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions. Solar Energy, 158, 490-503.
35. Kalogirou, S. A., 2001. Artificial neural networks in renewable energy systems applications: a review. Renewable and sustainable energy reviews, 5(4), 373-401.
36. Karaboga, D., & Basturk, B., 2007. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress, Springer, 789-798.
37. Karaboga, N., 2009. A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute, 346(4), 328-348.
38. Karaboga, D., 2010. Artificial bee colony algorithm. Scholarpedia, 5(3), 6915.
39. Karaboga, D., 2005. An idea based on honey bee swarm for numerical optimization Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, 200.
40. Kennedy, J., & Eberhart, R., 1995. PSO optimization. In Proc. IEEE Int. Conf. Neural Networks, IEEE Service Center, Piscataway, NJ, 4, 1941-1948.
41. Kumar, N., Hussain, I., Singh, B., & Panigrahi, B. K., 2017. Rapid MPPT for uniformly and partial shaded PV system by using JayaDE algorithm in highly fluctuating atmospheric conditions. IEEE Transactions on Industrial Informatics, 13(5), 2406-2416.
42. Liu, Y. H., Chen, J. H., & Huang, J. W., 2015. A review of maximum power point tracking techniques for use in partially shaded conditions. Renewable and Sustainable Energy Reviews, 41, 436-453.
43. Liu, L., Meng, X., & Liu, C., 2016. A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renewable and Sustainable Energy Reviews, 53, 1500-1507.
44. Malathy, S., & Ramaprabha, R. (2018). Reconfiguration strategies to extract maximum power from photovoltaic array under partially shaded conditions. Renewable and Sustainable Energy Reviews, 81, 2922-2934.
45. Mirjalili, S., Mirjalili, S. M., & Lewis, A., 2014. Grey wolf optimizer. Advances in engineering software, 69, 46-61.
46. Moballegh, S., & Jiang, J., 2014. Modeling, prediction, and experimental validations of power peaks of PV arrays under partial shading conditions. IEEE Transactions on Sustainable Energy, 5(1), 293-300.
47. Mohanty, S., Subudhi, B., & Ray, P. K., 2016. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy, 7(1), 181-188.
48. Mohanty, S., Subudhi, B., & Ray, P. K., 2017. A grey wolf-assisted perturb & observe MPPT algorithm for a PV system. IEEE Transactions on Energy Conversion, 32(1), 340-347.
49. Mohapatra, A., Nayak, B., Das, P., & Mohanty, K. B., 2017. A review on MPPT techniques of PV system under partial shading condition. Renewable and Sustainable Energy Reviews, 80, 854-867.
50. Momoh, J. A., 2015. Adaptive stochastic optimization techniques with applications. CRC Press.
51. Ngan, M. S., & Tan, C. W., 2011. A study of maximum power point tracking algorithms for stand-alone photovoltaic systems. In Applied Power Electronics Colloquium (IAPEC), 2011 IEEE, 22-27.
52. Panda, G., Pradhan, P. M., & Majhi, B., 2011. IIR system identification using cat swarm optimization. Expert Systems with Applications, 38(10), 12671-12683.
53. Rajasekar, N., Vysakh, M., Thakur, H. V., Azharuddin, S. M., Muralidhar, K., Paul, D., & Babu, T. S., 2014. Application of modified particle swarm optimization for maximum power point tracking under partial shading condition. Energy Procedia, 61, 2633-2639.
54. Ram, J. P., & Rajasekar, N., 2017. A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC). Energy, 118, 512-525.
55. Ram, J. P., Babu, T. S., & Rajasekar, N., 2017. A comprehensive review on solar PV maximum power point tracking techniques. Renewable and Sustainable Energy Reviews, 67, 826-847.
56. Ram, J. P., & Rajasekar, N., 2017. A novel flower pollination based global maximum power point method for solar maximum power point tracking. IEEE Transactions on Power Electronics, 32(11), 8486-8499.
57. Rao, R., 2016. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
58. Reisi, A. R., Moradi, M. H., & Jamasb, S., 2013. Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renewable and Sustainable Energy Reviews, 19, 433-443.
59. Rezk, H., Fathy, A., & Abdelaziz, A. Y., 2017. A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions. Renewable and Sustainable Energy Reviews, 74, 377-386.
60. Rizzo, S. A., & Scelba, G., 2015. ANN based MPPT method for rapidly variable shading conditions. Applied Energy, 145, 124-132.
61. Ross, T. J., 2009. Fuzzy logic with engineering applications. John Wiley & Sons.
62. Salam, Z., Ahmed, J., & Merugu, B. S., 2013. The application of soft computing methods for MPPT of PV system: A technological and status review. Applied Energy, 107, 135-148.
63. Saravanan, S., & Babu, N. R., 2016. Maximum power point tracking algorithms for photovoltaic system–A review. Renewable and Sustainable Energy Reviews, 57, 192-204.
64. Sawant, P. T., Lbhattar, P. C., & Bhattar, C. L., 2016. Enhancement of PV system based on artificial bee colony algorithm under dynamic conditions. In Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE International Conference on IEEE, 1251-1255.
65. Seyedmahmoudian, M., Horan, B., Soon, T. K., Rahmani, R., Oo, A. M. T., Mekhilef, S., & Stojcevski, A., 2016. State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems–a review. Renewable and Sustainable Energy Reviews, 64, 435-455.
66. Shaiek, Y., Smida, M. B., Sakly, A., & Mimouni, M. F., 2013. Comparison between conventional methods and GA approach for maximum power point tracking of shaded solar PV generators. Solar energy, 90, 107-122.
67. Smida, M. B., & Sakly, A., 2015. Genetic based algorithm for maximum power point tracking (MPPT) for grid connected PV systems operating under partial shaded conditions. In Modelling, Identification and Control (ICMIC), 2015 7th International Conference on IEEE, 1-6.
68. Sundareswaran, K., Sankar, P., Nayak, P. S. R., Simon, S. P., & Palani, S., 2015. Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony. IEEE transactions on sustainable energy, 6(1), 198-209.
69. Soufyane Benyoucef, A., Chouder, A., Kara, K., & Silvestre, S., 2015. Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Applied Soft Computing, 32, 38-48.
70. Storn, R., & Price, K., 1997. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
71. Subha, R., & Himavathi, S., 2017. MPPT of PV systems under partial shaded conditions using flower pollination algorithm. In Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT), 2017 International Conference on IEEE, 206-210.
72. Syafaruddin, Karatepe, E., Hiyama, T., 2009. Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions. IET Renewable Power Generation, 3(2), 239-253.
73. Taheri, H., Salam, Z., & Ishaque, K., 2010. A novel maximum power point tracking control of photovoltaic system under partial and rapidly fluctuating shadow conditions using differential evolution. In Industrial Electronics & Applications (ISIEA), 2010 IEEE Symposium on IEEE, 82-87.
74. Tajuddin, M. F. N., Ayob, S. M., & Salam, Z., 2012. Tracking of maximum power point in partial shading condition using differential evolution (DE). In Power and Energy (PECon), 2012 IEEE International Conference on IEEE, 384-389.
75. Tajuddin, M. F. N., Ayob, S. M., Salam, Z., & Saad, M. S., 2013. Evolutionary based maximum power point tracking technique using differential evolution algorithm. Energy and Buildings, 67, 245-252.
76. Teshome, D. F., Lee, C. H., Lin, Y. W., & Lian, K. L., 2017. A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading. IEEE Journal of Emerging and Selected Topics in Power Electronics, 5(2), 661-671.
77. Yang, X. S., & Deb, S., 2009. Cuckoo search via Lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on IEEE, 210-214.
78. Yang, X. S., 2010. Nature-inspired metaheuristic algorithms. Luniver press.
79. Yang, X. S., & Hossein Gandomi, A., 2012. Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29(5), 464-483.
80. Yang, X. S., 2013. Cuckoo search and firefly algorithm: theory and applications. Springer.
81. Yang, X. S., Karamanoglu, M., & He, X., 2013. Multi-objective flower algorithm for optimization. Procedia Computer Science, 18, 861-868.
82. Yang, X. S., Karamanoglu, M., & He, X., 2014. Flower pollination algorithm: a novel approach for multiobjective optimization. Engineering Optimization, 46(9), 1222-1237.
83. Yetayew, T. T., Jyothsna, T. R., & Kusuma, G., 2016. Evaluation of Incremental conductance and Firefly algorithm for PV MPPT application under partial shade condition. In Power Systems (ICPS), 2016 IEEE 6th International Conference on IEEE, 1-6.