A novel Prairie Dog Optimization Algorithm (PDOA) based MPPT controlling mechanism for grid-PV systems
Abstract
Recently, the solar photovoltaic (PV) systems are increasingly used in many application systems, due to their efficiency and cost-effectiveness. Still, extracting the maximum energy from PV panels under varying climatic conditions is one of the most significant problems. For this purpose, various optimization based MPPT controlling mechanisms are developed in the conventional works for obtaining the maximum energy yield. However, it suffers with the major problems of low convergence, computational burden, high time consumption to reach the optimal solution, and lack of efficiency. Therefore, this research work objects to implement a novel and recently developed optimization technique, named as, Prairie Dog Optimization Algorithm (PDOA) for MPPT controlling. With superior tracking efficiency and enhanced speed, it helps to extract the greatest power from the PV panels. In order to manage the output of PV with less switching stress and loss, a high gain interleaved SEPIC DC-DC converter is also used. A better power quality is ensured by using the voltage source inverter to lower harmonic distortion levels. The significance of the proposed study is to develop a novel optimization based MPPT controlling technique to obtain the maximum energy yield form the PV panels. The PDOA technique is not previously used for power extraction and MPP tracking, due to its intelligent best optimum solutions with high convergence, the proposed work uses the PDOA technique to optimally identify MPP. Also, the voltage boosting and conversion is performed with the use of high gain interleaved SEPIC converter with increased efficiency. Performance study evaluates and compares the simulation outcomes and the efficacy of the suggested regulating topology using a variety of metrics, including IV & PV characteristics, output voltage, output power, THD, grid output, peak time, settling time, and others.
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1. Mahesh PV, Meyyappan S, Alla RKR. A new multivariate linear regression MPPT algorithm for solar PV system with boost converter. ECTI Transactions on Electrical Engineering, Electronics, and Communications 2022; 20(2): 269–281. doi: 10.37936/ecti-eec.2022202.246909
2. Şahin ME, Blaabjerg F, Sangwongwanich A. A comprehensive review on supercapacitor applications and developments. Energies 2022; 15(3): 674. doi: 10.3390/en15030674
3. Pradhan C, Senapati MK, Ntiakoh NK, Calay RK. Roach infestation optimization MPPT algorithm for solar photovoltaic system. Electronics 2022; 11(6): 927. doi: 10.3390/electronics11060927
4. Premkumar M, Kumar C, Dharma Raj T, et al. A reliable optimization framework using ensembled successive history adaptive differential evolutionary algorithm for optimal power flow problems. IET Generation, Transmission & Distribution 2023; 17(6): 1333–1357. doi: 10.1049/gtd2.12738
5. Sangwongwanich A, Yang Y, Blaabjerg F. High-performance constant power generation in grid-connected PV systems. IEEE Transactions on Power Electronics 2015; 31(3): 1822–1825. doi: 10.1109/TPEL.2015.2465151
6. Khalid S. A novel Algorithm Adaptive Autarchoglossans Lizard Foraging (AALF) in a shunt active power filter connected to MPPT-based photovoltaic array. e-Prime-Advances in Electrical Engineering, Electronics and Energy 2023; 3: 100100. doi: 10.1016/j.prime.2022.100100
7. Yin M, Qin L, Wu G, Shi C. An improved variable step size photovoltaic MPPT algorithm based on perturb and observe method. In: Proceedings of Third International Conference on Electronics and Communication; Network and Computer Technology; 3–5 December 2021; Harbin, China. pp. 729–734.
8. Harish Kumar VC, Shanthi SA. Novel incremental conductive perturbation algorithm for improved output of MPPT. Mathematical Statistician and Engineering Applications 2022; 71(3s2): 1663–1682.
9. Dagal I, Akın B, Akboy E. MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink. Scientific Reports 2022; 12(1): 2664. doi: 10.1038/s41598-022-06609-6
10. Radhakrishnan RKG, Marimuthu U, Balachandran PK, et al. An intensified marine predator algorithm (MPA) for designing a solar-powered BLDC motor used in EV systems. Sustainability 2022; 14(21): 14120. doi: 10.3390/su142114120
11. Bouderres N, Kerdoun D, Djellad A, et al. Optimization of fractional order PI controller by PSO algorithm applied to a gridconnected photovoltaic system. Journal Européen des Systèmes Automatisés 2022; 55(4): 427–438. doi: 10.18280/jesa.550401
12. Chandrasekaran K, Sahayam JJ, Somasundram David Thanasingh SJ, et al. Performance of multifunctional smart PV-based domestic distributed generator in dual-mode operation. Machines 2021; 9(12): 356. doi: 10.3390/machines9120356
13. Bahgat ME. Fractional order PID controller design for maximum power point tracking of dynamic loaded PV system. International Journal of Advanced Engineering and Business Sciences 2022; 3(3): 91–106. doi: 10.21608/IJAEBS.2022.164919.1051
14. Karchi N, Kulkarni D, Pérez de Prado R, et al. Adaptive least mean square controller for power quality enhancement in solar photovoltaic system. Energies 2022; 15(23): 8909. doi: 10.3390/en15238909
15. Hamid B, Hussain I, Iqbal SJ, et al. Optimal MPPT and BES control for grid-tied DFIGURE-based wind energy conversion system. IEEE Transactions on Industry Applications 2022; 58(6): 7966–7977. doi: 10.1109/TIA.2022.3202757
16. Memon A, Wazir Bin Mustafa M, Anjum W, et al. Dynamic response and low voltage ride-through enhancement of brushless double-fed induction generator using Salp swarm optimization algorithm. PloS One 2022; 17(5): e0265611. doi: 10.1371/journal.pone.0265611
17. Sivarajan S, Jebaseelan SDS. Efficient adaptive deep neural network model for securing demand side management in IoT enabled smart grid. Renewable Energy Focus 2022; 42: 277–284. doi: 10.1016/j.ref.2022.08.003
18. Sangwongwanich A, Blaabjerg F. Mitigation of interharmonics in PV systems with maximum power point tracking modification. IEEE Transactions on power Electronics 2019; 34(9): 8279–8282. doi: 10.1109/TPEL.2019.2902880
19. Ali A, Almutairi K, Padmanaban S, et al. Investigation of MPPT techniques under uniform and non-uniform solar irradiation condition—A retrospection. IEEE Access 2020; 8: 127368–127392. doi: 10.1109/ACCESS.2020.3007710
20. Yang Y, Wang H, Sangwongwanich A, Blaabjerg F. Design for reliability of power electronic systems. In: Rashid MH (editor). Power Electronics Handbook. Butterworth-Heinemann; 2018. pp. 1423–1440.
21. Şahin ME, Okumuş Hİ. Comparison of different controllers and stability analysis for photovoltaic powered buck-boost DC-DC converter. Electric Power Components and Systems 2018; 46(2): 149–161. doi: 10.1080/15325008.2018.1436617
22. Sahin ME, Okumus HI. A fuzzy-logic controlled PV powered buck-boost DC-DC converter for battery-load system. In: Proceedings of the 2012 International Symposium on Innovations in Intelligent Systems and Applications; 2–4 July 2012; Trabzon, Turkey.
23. Şahin ME, Okumuş Hİ. Parallel-connected buck—Boost converter with FLC for hybrid energy system. Electric Power Components and Systems 2021; 48(19–20): 2117–2129. doi: 10.1080/15325008.2021.1913261
24. Şahin ME, Okumuş Hİ. Hydrogen production system design with synchronous buck converter. In: Proceedings of National Conference on Electrical, Electronics and Computer Engineering; 2–5 December 2010; Bursa, Turkey.
25. Subramaniana A, Raman J. Modified seagull optimization algorithm based MPPT for augmented performance of photovoltaic solar energy systems. Automatika: Časopis Za Automatiku, Mjerenje, Elektroniku, Računarstvo I Komunikacije 2022; 63(1): 1–15.
26. Ravindra S, Reddy AN, Tejaswi KNVS, Shamili KB. Comparative analysis of MPPT techniques using DC–DC converter topologies for PV systems. In: DC—DC Converters for Future Renewable Energy Systems. Springer; 2022. pp. 459–480.
27. Hichem L, Leila M, Amar O. Comparative study of perturb-and-observe and fuzzy logic MPPT for stand-alone PV system. In: Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities: Case Study: Tipasa, Algeria. Springer International Publishing; 2022. pp. 266–276.
28. Nagadurga T, Narasimham P, Vakula VS, Devarapalli R. Gray wolf optimization‐based optimal grid connected solar photovoltaic system with enhanced power quality features. Concurrency and Computation: Practice and Experience 2022; 34(5): e6696. doi: 10.1002/cpe.6696
29. Immanuel DG, Dayana DS, Sindarsingh Jebaseelan SD. Hybrid Genetic algorithm assisted artificial bee colony approach for voltage stability improvement. International Journal of Applied Engineering Research 2015; 10: 534–541.
30. Nyamathulla S, Chittathuru D, Muyeen SM. An overview of multilevel inverters lifetime assessment for grid-connected solar photovoltaic applications. Electronics 2023; 12(8): 1944. doi: 10.3390/electronics12081944
31. Mathew D, Naidu RC. A review on single-phase boost inverter technology for low power grid integrated solar PV applications. Ain Shams Engineering Journal 2023. doi: 10.1016/j.asej.2023.102365
32. Sekhar KSR, Chaudhari MA, Khadkikar V. Enhanced hybrid converter topology for PV-grid-EV integration. IEEE Transactions on Energy Conversion 2023. doi: 10.1109/TEC.2023.3287890
33. Rasekh MR, Jamwal PK, Gali V, Ahmadi MJ. Design and analysis of high gain DC-DC boost converter for grid connected solar photovoltaic system. In: Proceedings of 2023 International Conference on Power Electronics and Energy (ICPEE); 3–5 January 2023; Bhubaneswar, India.
34. Awan MMA, Javed MY, Asghar AB, Ejsmont K. Performance optimization of a ten check MPPT algorithm for an off-grid solar photovoltaic system. Energies 2022; 15(6): 2104. doi: 10.3390/en15062104
35. Yap KY, Sarimuthu CR, Lim JMY. Artificial intelligence based MPPT techniques for solar power system: A review. Journal of Modern Power Systems and Clean Energy 2020; 8(6): 1043–1059. doi: 10.35833/MPCE.2020.000159
36. Hai T, Zhou J, Muranaka K. An efficient fuzzy-logic based MPPT controller for grid-connected PV systems by farmland fertility optimization algorithm. Optik 2022; 267: 169636. doi: 10.1016/j.ijleo.2022.169636
37. Ebrahim MA, Osama A, Kotb KM, Bendary F. Whale inspired algorithm based MPPT controllers for grid-connected solar photovoltaic system. Energy Procedia 2019; 162: 77–86. doi: 10.1016/j.egypro.2019.04.009
38. Padmanaban S, Priyadarshi N, Holm-Nielsen JB, et al. A novel modified sine-cosine optimized MPPT algorithm for grid integrated PV system under real operating conditions. IEEE Access 2019; 7: 10467–10477. doi: 10.1109/ACCESS.2018.2890533
39. Tchaya GB, Kaoga DK, Alphonse S, Djongyang N. Optimization of the smart grids connected using an improved P&O MPPT algorithm and parallel active filters. Journal of Solar Energy Research 2021; 6(3): 814–828. doi: 10.22059/JSER.2021.320173.1196
40. Guo B, Su M, Sun Y, et al. Optimization design and control of single-stage single-phase PV inverters for MPPT improvement. IEEE Transactions on Power Electronics 2020; 35(12): 13000–13016. doi: 10.1109/TPEL.2020.2990923
41. Kim JC, Huh JH, Ko JS. Optimization design and test bed of fuzzy control rule base for PV system MPPT in micro grid. Sustainability 2020; 12(9): 3763. doi: 10.3390/su12093763
42. Ge X, Ahmed FW, Rezvani A, et al. Implementation of a novel hybrid BAT-Fuzzy controller based MPPT for grid-connected PV-battery system. Control Engineering Practice 2020; 98: 104380. doi: 10.1016/j.conengprac.2020.104380
43. Sibtain D, Gulzar MM, Shahid K, et al. Stability analysis and design of variable step-size P&O algorithm based on fuzzy robust tracking of MPPT for standalone/grid connected power system. Sustainability 2022; 14(15): 8986. doi: 10.3390/su14158986
44. Kerid R, Bounnah Y. Modeling and parameter estimation of solar photovoltaic based MPPT control using EKF to maximize efficiency. Bulletin of Electrical Engineering and Informatics 2022; 11(5): 2491–2499. doi: 10.11591/eei.v11i5.3782
45. Sundarsingh Jebaseelan SD, Muthu Selvan NB, Kumar C, et al. Solving constrained economic electrical energy generation and CO2 emission dispatch using hybrid algorithm. Environmental Technology & Innovation 2021; 24: 101999. doi: 10.1016/j.eti.2021.101999
46. Ezugwu AE, Agushaka JO, Abualigah L, et al. Prairie Dog Optimization Algorithm. Neural Computing and Applications 2022; 34(22): 20017–20065. doi: 10.1007/s00521-022-07530-9
47. Oliver JS, David PW, Balachandran PK, Mihet-Popa L. Analysis of grid-interactive PV-fed BLDC pump using optimized MPPT in DC–DC converters. Sustainability 2022; 14(12): 7205. doi: 10.3390/su14127205
DOI: https://doi.org/10.32629/jai.v7i1.810
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