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Differential Evolution Based Maximum Power Point Tracker for Photovoltaic Array Under Non-Uniform Illumination Condition


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1 Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia
     

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Photovoltaic system (PV) is an important technological asset for renewable energy production. It works by converting solar cell energy from the sun into electrical direct current. In reality, the photovoltaic module usually receives non-uniform solar irradiance at different light intensity due to non-atmospheric hindrance. Under such conditions, the PV system exhibits multiple peaks on the energy characteristic curve, generally known as the partial shading condition (PSC). Therefore, in order to maximize the energy harvested by the photovoltaic system (PV), maximum power point tracking (MPPT) method is suggested to extract all possible maxima that have been produced by the PV system under various circumstances through the non-uniform irradiance of the sunlight. Based on previous researches, it is found that conventional method such as perturb and observed (P&O) method failed to track the maximum power and was trapped at the local maximum power (LMPP). This paper focuses on exploring a metaheuristic method which is the differential evolution (DE) algorithm in optimizing the energy harvested by the PV system. The platform chosen for modelling in this paper is a 3 × 3 PV array. The PV array is tested with different conditions of partial shading where random irradiance values are set. Comparing the performance of PV between P&O and DE based MPPT controller, the DE based MPPT controller is inferred to have a higher success rate to escape from being trapped in LMPP and thus produce more total energy.

Keywords

Photovoltaic system, Maximum Power Point Tracking, Partial Shading Condition, Perturb and Observed, Differential Evolution.
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  • A. Razman, M.I. Normazlina and T.C. Wei, “Components Sizing of Photovoltaic Alone-System based on Loss of Power Supply Probability”, Renew Sustain Energy, Vol. 81, No. 2, pp. 136-321, 2018.
  • D.H. Muhsen, M. Nabil, H.T. Haider and T. Khatib, “A Novel Method for Sizing Standalone Photovoltaic System using Multi-Objective Differential Evolution Algorithm and Hybrid Multi-Criteria Decision Making Methods”, Energy, Vol. 174, pp. 1158-1175, 2019.
  • A.J. Morrison, “Global Demand Projections for Renewable Energy Resources”, Proceedings of IEEE Canada Conference on Electrical Power, pp. 1-6, 2007.
  • D. Singh, N.K. Sharma, Y.R. Sood and R.K. Jarial, “Global Status of Renewable Energy and Market: Future Prospectus and Target”, Proceedings of International Conference on Sustainable Energy and Intelligent Systems, pp. 233-239, 2011.
  • B. Kroposki, R. Margolis and D. Ton, “Harnessing the Sun”, IEEE Power Energy Magazine, Vol. 7, No. 3, pp. 22-32, 2009.
  • M. Liserre, T. Sauter and J.Y. Hung, “Future Energy Systems: Integrating Renewable Energy Sources into the Smart Power Grid through Industrial Electronics”, IEEE Industrial Electronics Magazine, Vol. 4, No. 1, pp. 18-37, 2010.
  • G. Petrone, G. Spagnuolo, R. Teodorescu and M. Veerachary, “Reliability Issues in Photovoltaic Power Processing Systems”, IEEE Transactions on Industrial Electronics, Vol. 55, No. 7, pp. 2569-2580, 2008.
  • S. Fawzan and M.A. Awadallah, “Detection and Assessment of Partial Shading in Photovoltaic Arrays”, Journal of Electrical Systems and Information Technology, Vol. 3, No. 1, pp. 23-32, 2016.
  • F. Belhachat and C. Larbes, “Review of Global Maximum Power Point Tracking Techniques of Photovoltaic System under Partial Shading Conditions”, Renewable and Sustainable Energy Reviews, Vol. 92, pp. 513-553, 2018.
  • M. Nasir and M.F. Zia, “Global Maximum Power Point Tracking Algorithm for Photovoltaic Systems under Partial Shading Conditions”, Proceedings of International Conference on International Power Electronics and Motion Control and Exposition, pp. 1-12, 2014.
  • U.S. Patel, D. Sahu and D. Tirkey, “Maximum Power Point Tracking using Perturb and Observe Algorithm and Compare with Another Algorithm”, International Journal of Digital Application and Contemporary Research, Vol. 2, No. 2, pp. 1-8, 2013.
  • A.H.M. Nordin and A.M. Omar, “Modeling and Simulation of Photovoltaic (PV) Array and Maximum Power Point Tracker (MPPT) for Grid-Connected PV System”, Proceedings of 3rd International Symposium and Exhibition in Sustainable Energy and Environment, pp. 1-14, 2011.
  • S. Kolesnik and A. Kuperman, “On the Equivalence of Major Variable-Step-Size MPPT Algorithms”, IEEE Journal of Photovoltaic, Vol. 6, No. 2, pp. 590-594, 2016.
  • C. Lee, K. Lim, M. Tan, R.K.Y. Chin and K. Teo, “A Genetic Algorithm for Management of Coding Resources in VANET”, Proceedings of International Conference on Automatic Control and Intelligent Systems, pp. 80-85, 2017.
  • L. Deyi and D. Yi, “Artificial Intelligence with Uncertainty”, 1st Edition, CRC Press, 2007.
  • K. Ishaque, Z. Zalam, M. Amjad and S. Mekhilef, “An Improved Particle Swarm Optimization (PSO)-based MPPT for PV with Reduced Steady-State Oscillation”, IEEE Transactions on Power Electronics, Vol. 27, No. 8, pp. 3627-3638, 2012.
  • A.F.D. Paulo and G.S. Porto, “Evolution of Collaborative Networks of Solar Energy Applied Technologies”, Journal of Cleaner Production, Vol. 204, pp. 310-320, 2018.
  • P. Choudhary and R.K. Srivastava, “Sustainability Perspectives-A Review for Solar Photovoltaic Trends and Growth Opportunities”, Journal of Cleaner Production, Vol. 227, pp. 589-612, 2019.
  • S. Nguyen, D. Thiruvady, A.T. Ernst and D. Alahakoon, “A Hybrid Differential Evolution Algorithm with Column Generation for Resource Constrained Job Scheduling”, Computers and Operation Research, Vol. 109, pp. 273-287, 2019.
  • N. Kumar, I. Hussain, B. Singh and B.K. Panigrahi, “Framework of Maximum Power Extraction from Solar PV Panel using Self Predictive Perturb and Observe Algorithm”, IEEE Transactions on Sustainable Energy, Vol. 9, No. 2, pp. 895-903, 2018.
  • R. Ahmad, A.F. Murtaza and H.A. Sher, “Power Tracking Techniques for Efficient Operation of Photovoltaic Array in Solar Application-A Review”, Renewable and Sustainable Energy Reviews, Vol. 101, pp. 82-102, 2019.
  • F. Belhachat and C. Larbes, “Comprehensive Review on Global Maximum Power Point Tracking Techniques for PV Systems Subjected to Partial Shading Conditions”, Solar Energy, Vol. 183, pp. 476-500, 2019.
  • K. Ishaque, Z. Salam and G. Lauss, “The Performance of Perturb and Observe and Incremental Conductance Maximum Power Point Tracking Method under Dynamic Weather Conditions”, Applied Energy, Vol. 119, pp. 228-236, 2014.
  • M. Choong, L. Angeline, R. Chin, K. Yeo and K. Teo, “Modeling of Vehicle Trajectory using K-Means and Fuzzy C-Means Clustering”, Proceedings of IEEE International Conference on Artificial Intelligence in Engineering and Technology, pp. 1-8, 2018.
  • T.K. Soon and S. Mekhilef, “Modified Incremental Conductance Algorithm for Photovoltaic System under Partial Shading Conditions and Load Variation”, IEEE Transactions on Industrial Electronics, Vol. 61, No. 10, pp. 5384-5392, 2014.
  • K. Ishaque and Z. Salam, “A Review of Maximum Power Point Tracking of PV System for Uniform Insolation and Partial Shading Condition”, Renewable and Sustainable Energy Reviews, Vol. 19, pp. 475-488, 2013.
  • N. Khehintung, T. Wiangtong and P. Sirsuk, “FPDA Implementation of MPPT using Variable Step-Size P&O Algorithm for PV Applications”, Proceedings of IEEE International Symposium on Communication and Information, pp. 212-215, 2006.
  • L. Abderezak, B. Aissa and S. Hamza, “Comparative Study of Three MPPT Algorithms for a Photovoltaic System Control”, Proceedings of World Congress on Information Technology and Computer Applications, pp. 1-5, 2015.
  • L. Lengyel, “Validating Rule-Based Algorithms”, Journal of Applied Science, Vol. 12, No. 4, pp. 59-75, 2015.
  • K. Ishaquel, Z. Salam, H. Taheri and A. Shamsudin, “Maximum Power Point Tracking for PV System under Partial Shading Condition via Particle Swarm Optimization”, Proceedings of IEEE Applied Power Electronics Colloquium, pp. 5-9, 2011.
  • M. El Shorbagy and A.E. Hassanien, “Particle Swarm Optimization from Theory of Application”, International Journal of Rough Sets and Data Analysis, Vol. 5, No. 2, pp. 1-14, 2018.
  • R. Haupt and S. Haupt, “Partial Genetic Algorithm”, 2nd Edition, Wiley and Sons, 2004.
  • H. Taheri, Z. Salam, K. Ishaque and Syafaruddin, “A Novel Maximum Power Point Tracking Control of Photovoltaic System under Partial and Rapidly Fluctuating Shadow Conditions using Differential Evolution”, Proceedings of IEEE Symposium on Industrial Electronics and Applications, pp. 82-87, 2010.
  • K.S. Tey, S. Mekhilef, M. Seyedmahmoudian, B. Horan and A. Stojcevski, “Improved Differential Evolution-Based MPPT Algorithm Using SEPIC for PV Systems Under Partial Shading Conditions and Load Variation”, IEEE Transactions on Industrial Informatics, Vol. 14, No. 10, pp. 4322-433, 2018.
  • A. Bidram, A. Davoudi and R. Balog, “Control and Circuit Techniques to Mitigate Partial Shading Effects in Photovoltaic Arrays”, IEEE Journal on Photovoltaics, Vol. 2, No. 4, pp. 532-546, 2012.
  • A. Mohapatra, B. Nayak, P. Das and K.B. Mohanty, “A Review on MPPT Techniques of PV System under Partial Shading Condition”, Renewable and Sustainable Energy Review, Vol. 80, pp. 854-867, 2017.

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  • Differential Evolution Based Maximum Power Point Tracker for Photovoltaic Array Under Non-Uniform Illumination Condition

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Authors

Nurul Izyan Kamaruddina
Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia
Ahmad Razani Haron
Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia
Bih Lii Chua
Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia
Min Keng Tan
Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia
Kit Guan Lim
Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia
Kenneth Tze Kin Teo
Modelling, Simulation and Computing Laboratory, Artificial Intelligence Research Unit, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia

Abstract


Photovoltaic system (PV) is an important technological asset for renewable energy production. It works by converting solar cell energy from the sun into electrical direct current. In reality, the photovoltaic module usually receives non-uniform solar irradiance at different light intensity due to non-atmospheric hindrance. Under such conditions, the PV system exhibits multiple peaks on the energy characteristic curve, generally known as the partial shading condition (PSC). Therefore, in order to maximize the energy harvested by the photovoltaic system (PV), maximum power point tracking (MPPT) method is suggested to extract all possible maxima that have been produced by the PV system under various circumstances through the non-uniform irradiance of the sunlight. Based on previous researches, it is found that conventional method such as perturb and observed (P&O) method failed to track the maximum power and was trapped at the local maximum power (LMPP). This paper focuses on exploring a metaheuristic method which is the differential evolution (DE) algorithm in optimizing the energy harvested by the PV system. The platform chosen for modelling in this paper is a 3 × 3 PV array. The PV array is tested with different conditions of partial shading where random irradiance values are set. Comparing the performance of PV between P&O and DE based MPPT controller, the DE based MPPT controller is inferred to have a higher success rate to escape from being trapped in LMPP and thus produce more total energy.

Keywords


Photovoltaic system, Maximum Power Point Tracking, Partial Shading Condition, Perturb and Observed, Differential Evolution.

References