PSO Training Neural Network MPPT with CUK Converter Topology for Stand-Alone PV Systems Under Varying Load and Climatic Conditions

Abstract

Temperature and irradiance levels are two examples of environmental variables that affect the power value produced by photovoltaic panels. Therefore, in order to transfer the maximum power value from the PV panel to the load under varying climatic conditions, maximum power point tracking (MPPT) algorithms and DC-DC converter topologies are used. In this study, the performances of boost converter and CUK converter circuit topologies are investigated under variable irradiance and variable load conditions by using a neural network-based MPPT algorithm learning particle swarm optimization (PSO). As the first scenario, it is analyzed assuming that the temperature and irradiance values coming to the panel are constant. As the second scenario, the performance evaluation of the converter topologies according to the current, voltage and power parameters is made for the variable load situation. As the last scenario, the difference in the irradiance value coming to the panel depending on the sun's condition during the day has been examined. Canadian Solar CS6P-250P PV panel is used in the study. 50 kHz is selected as the switching frequency. According to the results obtained, it has been observed that the CUK converter circuit topology reaches the maximum power point faster than the boost converter circuit topology both in dynamic environmental conditions and load change, and the oscillation at this point is less. It is aimed to increase the performance of this method, which uses boost converter topology and MPPT in the literature, by applying CUK converter topology.

Keywords

• Maximum power point tracking
• Neural network-based particle swarm optimization
• CUK converter
• Boost converter

References

1. Kamran M, Mudassar M, Fazal MR, Asghar MU, Bilal M, Asghar R. Implementation of improved Perturb & Observe MPPT technique with confined search space for standalone photovoltaic system. Journal of King Saud University-Engineering Sciences.2020; 32(7), 432-441.
2. Pillai DS, Ram JP, Ghias AM, Mahmud MA, Rajasekar N. An accurate, shade detection-based hybrid maximum power point tracking approach for PV systems. IEEE Transactions on Power Electronics. 2019; 35(6), 6594-6608.
3. Khan MJ, Pushparaj. A novel hybrid maximum power point tracking controller based on artificial intelligence for solar photovoltaic system under variable environmental conditions. Journal of Electrical Engineering & Technology. 2021; 16(4), 1879-1889.
4. Sundaram BM, Manikandan BV, Kumar BP, Winston DP. Combination of novel converter topology and improved MPPT algorithm for harnessing maximum power from grid connected solar PV systems. Journal of Electrical Engineering & Technology. 2019; 14, 733-746.
5. Padmavathi N, Chilambuchelvan A, Shanker NR. Maximum power point tracking during partial shading effect in PV system using machine learning regression controller. Journal of Electrical Engineering & Technology. 2021; 16, 737-748.
6. Kumari N, Kumar SS, Laxmi V. Design of an efficient bipolar converter with fast MPPT algorithm for DC nanogrid application. International Journal of Circuit Theory and Applications. 2021; 49(9), 2812-2839.
7. Thankakan R, Samuel Nadar ER. Investigation of the double input power converter with N stages of voltage multiplier using PSO‐based MPPT technique for the thermoelectric energy harvesting system. International Journal of Circuit Theory and Applications. 2020; 48(3), 435-448.
8. Kofinas P, Dounis AI, Papadakis G, Assimakopoulos MN. An Intelligent MPPT controller based on direct neural control for partially shaded PV system. Energy and Buildings. 2015; 90, 51-64.

9. Kumar R, Khandelwal S, Upadhyay P, Pulipaka S. Global maximum power point tracking using variable sampling time and pv curve region shifting technique along with incremental conductance for partially shaded photovoltaic systems. Solar Energy. 2019; 189, 151-178.
10. Bouarroudj N, Benlahbib B, Houam Y, Sedraoui M, Batlle VF, Abdelkrim T, et al. Fuzzy based incremental conductance algorithm stabilized by an optimal integrator for a photovoltaic system under varying operating conditions. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2021; 1-26.
11. Nasir A, Rasool I, Sibtain D, Kamran R. Adaptive Fractional Order PID Controller Based MPPT for PV Connected Grid System Under Changing Weather Conditions. Journal of Electrical Engineering & Technology. 2021; 16(5), 2599-2610.
12. Jiang M, Ghahremani M, Dadfar S, Chi H, Abdallah YN, Furukawa N. A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system. Control Engineering Practice. 2021; 114, 104880.
13. Hamdi H, Regaya C B, Zaafouri A. Real-time study of a photovoltaic system with boost converter using the PSO-RBF neural network algorithms in a MyRio controller. Solar energy. 2019; 183, 1-16.
14. Saidi AS, Salah CB, Errachdi A, Azeem MF, Bhutto JK, Ijyas VT. A novel approach in stand-alone photovoltaic system using MPPT controllers & NNE. Ain Shams Engineering Journal. 2021; 12(2), 1973-1984.
15. Fathi M, Parian JA. Intelligent MPPT for photovoltaic panels using a novel fuzzy logic and artificial neural networks based on evolutionary algorithms. Energy Reports. 2021; 7, 1338-1348.
16. Yılmaz M, Çorapsız, MF. Artificial Neural Network based MPPT Algorithm with Boost Converter topology for Stand-Alone PV System. Erzincan University Journal of Science and Technology. 2022; 15(1), 242-257.
17. Yılmaz M, Kaleli A, Çorapsız MF. Machine learning based dynamic super twisting sliding mode controller for increase speed and accuracy of MPPT using real-time data under PSCs. Renewable Energy. 2023; 219, 119470.
18. Ghazi GA, Hasanien HM, Al-Ammar EA, Turky RA, Ko W, Park S, Choi HJ. African vulture optimization algorithm-based PI controllers for performance enhancement of hybrid renewable-energy systems. Sustainability. 2022; 14(13), 8172.
19. Houam Y, Terki A, Bouarroudj N. An efficient metaheuristic technique to control the maximum power point of a partially shaded photovoltaic system using crow search algorithm (CSA). Journal of Electrical Engineering & Technology. 2021; 16, 381-402.
20. Aygül K, Cikan M, Demirdelen T, Tumay M. Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2023; 45(3), 8337-8355.
21. Tao H, Ghahremani M, Ahmed FW, Jing W, Nazir MS, Ohshima K. A novel MPPT controller in PV systems with hybrid whale optimization-PS algorithm based ANFIS under different conditions. Control Engineering Practice. 2021; 112, 104809.
22. Pervez I, Shams I, Mekhilef S, Sarwar A, Tariq M, Alamri B. Most valuable player algorithm based maximum power point tracking for a partially shaded PV generation system. IEEE Transactions on Sustainable Energy. 2021; 12(4), 1876-1890.
23. Fares D, Fathi M, Shams I, Mekhilef S. A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions. Energy Conversion and Management. 2021; 230, 113773.
24. Mohammadinodoushan M, Abbassi R, Jerbi H, Ahmed FW, Rezvani A. A new MPPT design using variable step size perturb and observe method for PV system under partially shaded conditions by modified shuffled frog leaping algorithm-SMC controller. Sustainable Energy Technologies and Assessments. 2021; 45, 101056.
25. Aldosary A, Ali ZM, Alhaider MM, Ghahremani M, Dadfar S, Suzuki K. A modified shuffled frog algorithm to improve MPPT controller in PV System with storage batteries under variable atmospheric conditions. Control Engineering Practice. 2021; 112, 104831.
26. Eltamaly AM, Al-Saud MS, Abokhalil AG. A novel bat algorithm strategy for maximum power point tracker of photovoltaic energy systems under dynamic partial shading. IEEE Access. 2020; 8, 10048-10060.
27. Mansoor M, Mirza AF, Ling Q. Harris hawk optimization-based MPPT control for PV systems under partial shading conditions. Journal of Cleaner Production. 2020; 274, 122857.
28. Zafar MH, Khan NM, Mirza AF, Mansoor M, Akhtar N, Qadir MU, ... & Moosavi, et al. A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustainable Energy Technologies and Assessments. 2021; 47, 101367.
29. Basha CH, Murali M. A new design of transformerless, non‐isolated, high step‐up DC‐DC converter with hybrid fuzzy logic MPPT controller. International Journal of Circuit Theory and Applications. 2022; 50(1), 272-297.
30. Kumar N, Hussain I, Singh B, Panigrahi BK. Rapid MPPT for uniformly and partial shaded PV system by using JayaDE algorithm in highly fluctuating atmospheric conditions. IEEE Transactions on Industrial Informatics. 2017; 13(5), 2406-2416.
31. Alshareef M, Lin Z, Ma M, Cao W. Accelerated particle swarm optimization for photovoltaic maximum power point tracking under partial shading conditions. Energies. 2019; 12(4), 623.
32. Bounabi M, Kaced K, Ait-Cheikh MS, Larbes C, Ramzan N. Modelling and performance analysis of different multilevel inverter topologies using PSO-MPPT technique for grid connected photovoltaic systems. Journal of Renewable and Sustainable Energy. 2018; 10(4).
33. Eltamaly AM, Farh HM, Abokhalil AG. A novel PSO strategy for improving dynamic change partial shading photovoltaic maximum power point tracker. Energy sources, part a: recovery, utilization, and environmental effects. 2020; 1-15.
34. Kamarzaman NA, Tan CW. A comprehensive review of maximum power point tracking algorithms for photovoltaic systems. Renewable and Sustainable Energy Reviews. 2014; 37, 585-598.
35. Pal RS, Mukherjee V. Metaheuristic based comparative MPPT methods for photovoltaic technology under partial shading condition. Energy. 2020; 212, 118592.
36. Al-Majidi SD, Abbod MF, Al-Raweshidy HS. A particle swarm optimisation-trained feedforward neural network for predicting the maximum power point of a photovoltaic array. Engineering Applications of Artificial Intelligence. 2020; 92, 103688.
37. Reddy SS, Yammani C. A novel two step method to extract the parameters of the single diode model of Photovoltaic module using experimental Power–Voltage data. Optik. 2021; 248, 167977.
38. Çevik K, Koçer H. A Soft Computing Application Based on Artificial Neural Networks Training by Particle Swarm Optimization. Süleyman Demirel University Journal of Natural and Applied Sciences. 2013; 17(2), 39-45.