Analysis of Single Diode Model of Solar Cell Simulated with MATLAB for Maximum Electric Power Extraction

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : Itisha Singh, Gaurav Gupta

10.14445/23488379/IJEEE-V11I7P108

How to Cite?

Itisha Singh, Gaurav Gupta, "Analysis of Single Diode Model of Solar Cell Simulated with MATLAB for Maximum Electric Power Extraction," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 7, pp. 102-112, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I7P108

Abstract:

The quest for efficient utilization of solar energy has led to significant advancements in photovoltaic technology. Understanding the behavior of solar cells under varying conditions is crucial for optimizing their performance. The single-diode model serves as a fundamental tool for simulating solar cell characteristics and extracting maximum electric power. In this study, the paper presents a comprehensive analysis of the single diode model implemented in MATLAB for electric power extraction. The study begins with an overview of the theoretical foundations of the single diode model, elucidating the key parameters that govern solar cell behavior. Subsequently, the MATLAB simulation framework is described, detailing the implementation of the model equations and the numerical techniques employed for accurate computation. The analysis encompasses a range of operating conditions, including variations in irradiance and temperature, which profoundly influence solar cell performance. Through systematic simulation experiments, the impact of these factors on key metrics such as the Current-Voltage (I-V) curve, Power-Voltage (P-V) curve, and efficiency are examined. Furthermore, the study explores optimization strategies aimed at maximizing electric power extraction from the solar cell. Techniques such as Maximum Power Point Tracking (MPPT) algorithms are investigated to enhance energy harvesting efficiency under dynamic environmental conditions.

Keywords:

SDM - Single Diode Model, SC - Solar Cell, EPE - Electric Power Extraction, MS - MATLAB Simulation, MPPT - Maximum Power Point Tracking, PS - Photovoltaic Systems, TE - Temperature Effects, I-V curves - Current-Voltage curve.

References:

[1] A. Elmitwally, S. Abdelkader, and M. Elkateb, “Performance Evaluation of Fuzzy Controlled Three and Four Wireshunt Active Power Conditioners,” 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings, Singapore, pp. 1650-1655, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Dalila Beriber, and Abdelaziz Talha, “MPPT Techniques for PV Systems,” 4 th International Conference on Power Engineering, Energy and Electrical Drives, Istambul, Turkey, pp. 1437-1442, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Azmi Saleh et al., “Comparison of MPPT Fuzzy Logic Controller Based on Perturb and Observe (P&O) and Incremental Conductance (InC) Algorithm on Buck-Boost Converter,” 2018 2nd International Conference on Electrical Engineering and Informatics (ICon EEI), Batam, Indonesia, pp. 154-158, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Nasir Hussein Selman, and Jawad Radhi Mahmood, “Comparison between Perturb & Observe, Incremental Conductance and Fuzzy Logic MPPT Techniques at Different Weather Conditions,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 5, no. 7, pp. 12556-12569, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[5] R.Sridhar et al., “Modeling of PV Array and Performance Enhancement by MPPT Algorithm,” International Journal of Computer Applications, vol. 7, no. 5, pp. 35-39, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Hairul Nissah Zainudin, and Saad Mekhilef, “Comparison Study of Maximum Power Point Tracker Techniques for PV Systems,” Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo, Egypt, pp. 750-755, 2010.
[Google Scholar] [Publisher Link]
[7] Mostafa Barzegar Kalashani, and Murtaza Farsadi, “New Structure for Photovoltaic Systems with Maximum Power Point Tracking Ability,” International Journal of Power Electronics and Drive System, vol. 4, no. 4, pp. 489-498, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] M. Berrera et al., “Experimental Test of Seven Widely-Adopted MPPT Algorithms,” 2009 IEEE Bucharest PowerTech, Bucharest, Romania, pp. 1-8, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Lakshmi P.N. Jyothy, and M.R. Sindhu, “An Artificial Neural Network Based MPPT Algorithm for Solar PV System,” 2018 4th International Conference on Electrical Energy Systems (ICEES), Chennai, India, pp. 375-380, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ali Reza Reisi, Mohammad Hassan Moradi, and Shahriar Jamasb, “Classification and Comparison of Maximum Power Point Tracking Techniques for the Photovoltaic System: A Review,” Renewable and Sustainable Energy Reviews, vol. 19, pp. 433-443, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Bidyadhar Subudhi, and Raseswari Pradhan, “A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems,” IEEE Transactions on Sustainable Energy, vol. 4, no. 1, pp. 89-98, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Hyeon-Seok Lee, and Jae-Jung Yun, “Advanced MPPT Algorithm for Distributed Photovoltaic Systems,” Energies, vol. 12, no. 18, pp. 1-17, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Farhad Khosrojerdi, Shamsodin Taheri, and Ana-Maria Cretu, “An Adaptive Neuro-Fuzzy Inference System-Based MPPT Controller for Photovoltaic Arrays,” 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, Canada, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Rayane Hijazi, and Nabil Karami, “Neural Network Assisted Variable-Step-Size P&O for Fast Maximum Power Point Tracking,” 2020 32nd International Conference on Microelectronics (ICM), Aqaba, Jordan, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] S. Narendiran et al., “Fuzzy Logic Controller Based Maximum Power Point Tracking for PV System,” 2016 3rd International Conference on Electrical Energy Systems (ICEES), Chennai, India, pp. 29-34, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ahmed G. Abdullah, Mothanna Sh. Aziz, and Bashar Abdullah Hamad, “Comparison between Neural Network and P&O Method in Optimizing MPPT Control for Photovoltaic Cell,” International Journal of Electrical and Computer Engineering, vol. 10, no. 5, pp. 5083- 5092, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Mohammad Sarvi, and Ahmad Azadian, “A Comprehensive Review and Classified Comparison of MPPT Algorithms in PV Systems,” Energy Systems, vol. 13, pp. 281-320, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Parag K. Atri, P.S. Modi, and Nikhil Shashikant Gujar, “Comparison of Different MPPT Control Strategies for Solar Charge Controller,” 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, pp. 65-69, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Ihssane Chtouki, Patrice Wira, and Malika Zazi, “Comparison of Several Neural Network Perturb and Observe MPPT Methods for Photovoltaic Applications,” 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, pp. 909-914, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mahyar Khosravi et al., “A Novel Hybrid Model-Based MPPT Algorithm Based on Artificial Neural Networks for Photovoltaic Applications,” 2017 IEEE Southern Power Electronics Conference (SPEC), Puerto Varas, Chile, pp. 1-6, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Naveen, and Anil Kumar Dahiya, “Implementation and Comparison of Perturb & Observe ANN and ANFIS Based MPPT Techniques,” 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, pp. 1-5, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Nora Kacimi et al., “New Improved Hybrid MPPT Based on Neural Network-Model Predictive Control-Kalman Filter for Photovoltaic System,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 3, pp. 1230-1241, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Kannabiran Kanimozhi, and Bagarudeen Raja Mohamed Rabi, “Development of Hybrid MPPT Algorithm for Maximum Power Harvesting under Partial Shading Conditions,” Circuits and Systems, vol. 7, no. 8, pp. 1611-1622, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Khaled Bataineh, “Improved Hybrid Algorithms-Based MPPT Algorithm for PV System Operating under Severe Weather Conditions,” IET Power Electronics, vol. 12, no. 4, pp. 703-711, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Gianluca Aurilio et al., “Fast Hybrid MPPT Technique for Photovoltaic Applications: Numerical and Experimental Validation,” Advances Power Electronics, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Sajid Sarwar et al., “A Novel Hybrid MPPT Technique to Maximize Power Harvesting from PV System under Partial and Complex Partial Shading,” Applied Sciences, vol. 12, no. 2, pp. 1-26, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Ratnakar Babu Bollipo, Suresh Mikkili, and Praveen Kumar Bonthagorla, “Hybrid, Optimization, Intelligent and Classical PV MPPT Techniques: A Review,” CSEE Journal of Power and Energy Systems, vol. 7, no. 1, pp. 9-33, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Said Azzouz, Sabir Messalti, and Abdelghani Harrag, “A Novel Hybrid MPPT Controller Using (P&O)-Neural Networks for Variable Speed Wind Turbine Based on DFIG,” Modelling, Measurement and Control A, vol. 92, no. 1, pp. 23-29, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[29] M. Arjun, and J.B. Zubin, “Artificial Neural Network Based Hybrid MPPT for Photovoltaic Modules,” 2018 International CET Conference on Control, Communication, and Computing (IC4), Thiruvananthapuram, India, pp. 140-145, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Hadeed Ahmed Sher et al., “A New Sensorless Hybrid MPPT Algorithm Based on Fractional Short-Circuit Current Measurement and P&O MPPT,” IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1426-1434, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[31] B. Pakkiraiah, and G. Durga Sukumar, “Research Survey on Various MPPT Performance Issues to Improve the Solar PV System Efficiency,” Journal of Solar Energy, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Laxman Bhukya, Narender Reddy Kedika, and Surender Reddy Salkuti, “Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review,” Algorithms, vol. 15, no. 10, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Muhammad Rameez Javed et al., “Comparison of the Adaptive Neural-Fuzzy Interface System (ANFIS) Based Solar Maximum Power Point Tracking (MPPT) with other Solar MPPT Methods,” 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Moirangthem Dennis Singh, V.J. Shine, and Varaprasad Janamala, “Application of Artificial Neural Networks in Optimizing MPPT Control for Standalone Solar PV System,” 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India, pp. 162-166, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Jobeda Khanam, and Simon Y. Foo, “Neural Networks Technique for Maximum Power Point Tracking of Photovoltaic Array,” SoutheastCon 2018, St. Petersburg, USA, pp. 1-4, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Wafa Hayder et al., “On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions,” Energies, vol. 15, no. 20, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Idriss Dagal, Burak Akın, and Erdem Akboy, “MPPT Mechanism Based on Novel Hybrid Particle Swarm Optimization and Salp Swarm Optimization Algorithm for Battery Charging through Simulink,” Scientific Reports, vol. 12, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Ziad M. Ali et al., “Novel Hybrid Improved Bat Algorithm and Fuzzy System Based MPPT for Photovoltaic under Variable Atmospheric Conditions,” Sustainable Energy Technologies and Assessments, vol. 52, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Linjuan Gong, Guolian Hou, and Congzhi Huang, “A Two-Stage MPPT Controller for PV System Based on the Improved Artificial Bee Colony and Simultaneous Heat Transfer Search Algorithm,” ISA Transactions, vol. 132, pp. 428-443, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Saibal Manna et al., “A Novel MRAC-MPPT Scheme to Enhance Speed and Accuracy in PV Systems,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 47, pp. 233-254, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Saibal Manna et al., “A Novel Robust Model Reference Adaptive MPPT Controller for Photovoltaic Systems,” Scientia Iranica, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Abd Essalam Badoud, Saad Mekhilef, and Belkacem Ould Bouamama, “A Novel Hybrid MPPT Controller Based on Bond Graph and Fuzzy Logic in Proton Exchange Membrane Fuel Cell System: Experimental Validation,” Arabian Journal for Science and Engineering, vol. 47, pp. 3201-3220, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[43] V. Vinothkumar, and R. Kanimozhi, “Power Quality Improvement by PV Integrated UPQC Using a Multi-Level Inverter with Resilient Back Propagation Neural Network Approach,” Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6277-6294, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Farzad Rastegar, and Zohreh Paydar, “Improving the Performance of Unified Power Quality Conditioner Using Interval Type 2 Fuzzy Control,” 2022 30th International Conference on Electrical Engineering (ICEE), Tehran, Iran, pp. 437-441, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[45] S. Rechka et al., “A Comparative Study of Harmonic Detection Algorithms for Active Filters and Hybrid Active Filters,” 2002 IEEE 33rd Annual Power Electronics Specialists Conference, Cairns, Australia, vol. 1, pp. 357-363, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Yujia Shang, and Aiguo Wu, “TS-Fuzzy-Controlled Shunt Active-Power Filter for Power Quality Improvement,” 2011 International Conference on Electrical and Control Engineering, Yichang, China, pp 1869-1872, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[47] A. Hamadi et al., “Comparison of Fuzzy Logic and Proportional Integral Controller of Voltage Source Active Filter Compensating Current Harmonics and Power Factor,” 2004 IEEE International Conference on Industrial Technology, Hammamet, Tunisia, vol. 2, pp. 645-650, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[48] H.L. Jou, J.C. Wu, and H.Y. Chu, “New Single-Phase Active Power Filter,” IEE Proceedings - Electric Power Applications, vol. 141, no. 3, pp. 129-134, 1994.
[CrossRef] [Google Scholar] [Publisher Link]