臺灣博碩士論文加值系統

Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some meta-heuristic (MH) algorithm to track the multiple peak P - V curve of partially shaded PV system.
Cuckoo Search (CS) is a new optimization algorithm based on MH approach. It has been used to solve optimization problems in many applications including Maximum Power Point Tracking (MPPT) problem. CS algorithm performs well in tracking the Global Maximum Power Point (GMPP). However, just like any other MH algorithms, there is still a dilemmatic trading between their accuracy and the tracking time needed to find Global Maximum Power Point (GMPP).
This thesis proposes a new MPPT algorithm by combining CS algorithm with Golden Section Search (GSS) to take beneficial features from both algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental result show a noticeable performance improvement compared to original CS algorithm and other MH algorithms.

Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some meta-heuristic (MH) algorithm to track the multiple peak P - V curve of partially shaded PV system.
Cuckoo Search (CS) is a new optimization algorithm based on MH approach. It has been used to solve optimization problems in many applications including Maximum Power Point Tracking (MPPT) problem. CS algorithm performs well in tracking the Global Maximum Power Point (GMPP). However, just like any other MH algorithms, there is still a dilemmatic trading between their accuracy and the tracking time needed to find Global Maximum Power Point (GMPP).
This thesis proposes a new MPPT algorithm by combining CS algorithm with Golden Section Search (GSS) to take beneficial features from both algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental result show a noticeable performance improvement compared to original CS algorithm and other MH algorithms.

Abstract i
Acknowledgements ii
Tables of Contents iii
List of Figures iv
List of Tables vi
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Problem Statement 3
1.3 Methodology 3
1.4 Outline 4
CHAPTER 2 PV MODEL & PARTIAL SHADING PROBLEM 5
2.1 PV Model 5
2.2 Partial Shading Problem 6
CHAPTER 3 REVIEW OF SOME MPPT ALGORITHMS 10
3.1 Cuckoo Search (CS) 10
3.2 Golden Section Search (GSS) 13
3.3 Particle Swarm Optimization (PSO) 15
CHAPTER 4 PROPOSED METHOD 18
4.1 The Proposed Method 18
4.2 Tracking Example of the Proposed Method 23
CHAPTER 5 EVALUATION RESULT 26
5.1 Testing Preparation and Parameter Selection 26
5.2 Simulation Result 30
5.3 Experimental Set Up and Result 37
CHAPTER 6 CONCLUSION & FUTURE WORK 47
6.1 Conclusion 47
6.2 Future Work 47
REFERENCE 48

[1] N. Kannan and D. Vakeesan, “Solar energy for future world: - a review,” Renewable and Sustainable Energy Reviews, vol. 62, pp. 1092 – 1105, 2016.
[2] T. Covert, M. Greenstone, and C. R. Knittel, “Will we ever stop using fossil fuels?” Journal of Economic Perspectives, vol. 30, no. 1, pp. 117–38, February 2016.
[3] R. Jiang, Y. Han, and S. Zhang, “Wide-range, high-precision and low complexity MPPT circuit based on perturb and observe algorithm,” Electronics Letters, vol. 53, no. 16, pp. 1141–1142, 2017.
[4] G. Hsieh, H. Hsieh, C. Tsai, and C. Wang, “Photovoltaic power increment-aided incremental-conductance MPPT with two-phased tracking,” IEEE Transactions on Power Electronics, vol. 28, no. 6, pp. 2895–2911, June 2013.
[5] Y. Liu, L. Chen, L. Chen, H. Xin, and D. Gan, “A newton quadratic interpolation based control strategy for photovoltaic system,” in International Conference on Sustainable Power Generation and Supply (SUPERGEN 2012, pp. 1–6), Sept 2012.
[6] R. Shao, R. Wei, and L. Chang, “A multi-stage MPPT algorithm for PV systems based on golden section search method,” in 2014 IEEE Applied Power Electronics Conference and Exposition - APEC 2014, pp. 676–683, March 2014.
[7] M. S. Ngan and C. W. Tan, “A study of maximum power point tracking algorithms for stand-alone photovoltaic systems,” in 2011 IEEE Applied Power Electronics Colloquium (IAPEC), pp. 22–27, April 2011.
[8] M. Danandeh and S. M. G., “Comparative and comprehensive review of maximum power point tracking methods for pv cells,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 2743 – 2767, 2018.
[9] Y. Liu, Z. Pang, and Z. Cheng, “Research on an adaptive solar photovoltaic array using shading degree model-based reconfiguration algorithm,” in 2010 Chinese Control and Decision Conference, pp. 2356–2360, May 2010.
[10] N. Femia, G. Lisi, G. Petrone, G. Spagnuolo, and M. Vitelli, “Distributed maximum power point tracking of photovoltaic arrays: Novel approach and system analysis,” IEEE Transactions on Industrial Electronics, vol. 55, no. 7, pp. 2610–2621, July 2008.
[11] J. Jiahui, Q. Yanhui, and C. Daolian, “A distributed maximum power point tracking flyback type pv grid-connected inverter,” in IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, pp. 7713–7717, Oct 2017.
[12] I. Abdalla, J. Corda, and L. Zhang, “Multilevel dc-link inverter and control algorithm to overcome the pv partial shading,” IEEE Transactions on Power Electronics, vol. 28, no. 1, pp. 14–18, Jan 2013.
[13] L. F. L. Villa, T. Ho, J. Crebier, and B. Raison, “A power electronics equalizer application for partially shaded photovoltaic modules,” IEEE Transactions on Industrial Electronics, vol. 60, no. 3, pp. 1179–1190, March 2013.
[14] M. M. Algazar, H. Al-Monier, H. A. El-Halim, and M. E. E. K. Salem, “Maximum power point tracking using fuzzy logic control,” International Journal of Electrical Power and Energy Systems, vol. 39, no. 1, pp. 21 – 28, 2012.
[15] N. Khaehintung, K. Pramotung, B. Tuvirat, and P. Sirisuk, “Riscmicrocontroller built-in fuzzy logic controller of maximum power point tracking for solar-powered light-flasher applications,” in 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004, vol. 3, pp. 2673–2678 Vol. 3. , Nov 2004
[16] K. Huang, W. Li, and X. Huang, “Mppt of solar energy generating system with fuzzy control and artificial neural network,” in 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, vol. 1, pp. 230–233, Sept 2011.
[17] Y. Liu, S. Huang, J. Huang, and W. Liang, “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, pp. 1027–1035, Dec 2012.
[18] S. K. Sahoo, M. Balamurugan, S. Anurag, R. Kumar, and V. Priya, “Maximum power point tracking for pv panels using ant colony optimization,” in 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1–4, April 2017.
[19] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983.
[20] M. A. Azam, S. Abdullah-Al-Nahid, M. A. Kabir, and S. M. H. Chowdhury, “Microcontroller based maximum power tracking of PV using stimulated annealing algorithm,” in 2012 International Conference on Informatics, Electronics Vision (ICIEV), pp. 298–303, May 2012.
[21] J. Ahmed and Z. Salam, “A maximum power point tracking (MPPT) for PV system using cuckoo search with partial shading capability,” Applied Energy, vol. 119, pp. 118 – 130, 2014.
[22] H. A. B. Siddique, P. Xu, and R. W. De Doncker, “Parameter extraction algorithm for one-diode model of pv panels based on datasheet values,” in 4th International Conference on Clean Electrical Power: Renewable Energy Resources Impact, ICCEP 2013, Alghero, Italy, pp. 7 –13, 2013.
[23] J. Cubas, S. Pindado, and A. Farrahi, “New method for analytical photovoltaic parameter extraction,” Proceedings of 2013 International Conference on Renewable Energy Research and Applications, ICRERA 2013. Madrid, Spain, pp. 873– 877, 2013.
[24] M. Seyedmahmoudian, B. Horan, T. K. Soon, R. Rahmani, A. M. Than Oo, S. Mekhilef, and A. Stojcevski, “State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems a review,” Renewable and Sustainable Energy Reviews, vol. 64, no. C, pp. 435–455, 2016.
[25] X. Yang and S. Deb, “Cuckoo search via lévy flights,” in 2009 World Congress on Nature Biologically Inspired Computing (NaBIC), pp. 210–214, Dec 2009.
[26] X.-S. Yang and S. Deb, “Multiobjective cuckoo search for design optimization,” Computers and Operations Research, vol. 40, no. 6, pp.1616 – 1624, 2013.
[27] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, pp. 1942-1948, 1995.
[28] D. F. Teshome, C. H. Lee, Y. W. Lin, and K. L. Lian, “A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 5, no. 2, pp. 661–671, June 2017.