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Improving MPPT Performance in PV Systems Based on Integrating the Incremental Conductance and Particle Swarm Optimization Methods

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Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

For optimum performance and efficiency of photovoltaic (PV) systems, the maximum power point tracking (MPPT) methods are utilized. Because of the dramatic growth of photovoltaic systems, several MPPT approaches have been proposed in recent decades. Regarding this, we have benefitted from the two methods of incremental conductance (IC) and particle swarm optimization (PSO). The IC method holds a high convergence rate in finding the MPP. However, at constant radiation, the ripple is highly potent. The PSO control method has a low convergence rate at finding the maximum power point (MPP); however, it is stable in constant radiation and has a much lower ripple output power than the IC method. Therefore, by integrating and combining these two methods and using their advantages, a new strategy for improving the MPPT has been provided. The proposed method benefits the convergence speed of the IC method in radiative changes, and it profits the stability and high accuracy of the PSO method in constant irradiation conditions. We tested and compared the three control methods using MATLAB/Simulink and obtained the required results. Moreover, the ESP32 board was employed to get the experimental results and so was the C +  + /Arduino coding space for coding and using ESP32. The experimental results confirm the effectiveness of the proposed method in terms of high convergence and high accuracy.

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References

  • Ahmed M, Abdelrahem M, Hackl CM and Kennel R (2020) Direct switching maximum power point tracking technique for PV applications. PESS 2020 IEEE Power and Energy Student Summit, Online, 2020, pp 1–5

  • Alagammal S, Rathina Prabha N (2020) Combination of modified P&O with power management circuit to exploit reliable power from autonomous PV-battery systems. Iran J Sci Technol Trans Electr Eng. https://doi.org/10.1007/s40998-020-00346-0

    Article  Google Scholar 

  • Alagammal S, Rathina Prabha N (2021) Combination of modified P&O with power management circuit to exploit reliable power from autonomous PV-battery systems. Iran J Sci Technol Trans Electr Eng 45:97–114. https://doi.org/10.1007/s40998-020-00346-0

    Article  Google Scholar 

  • Ali AI, Sayed MA, Mohamed EE (2018) Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system. Int J Electr Power Energy Syst 99:192–202

    Article  Google Scholar 

  • Boukenoui R and Mellit A (2019) Applications of improved versions of fuzzy logic based maximum power point tracking for controlling photovoltaic systems. In Solar Photovoltaic Power Plants. Springer. p. 143–164

  • Danandeh M (2018) Comparative and comprehensive review of maximum power point tracking methods for PV cells. Renew Susta Energy Rev 82:2743–2767

    Article  Google Scholar 

  • Dorofte C, Borup U and Blaabjerg F (2005) A combined two-method MPPT control scheme for grid-connected photovoltaic systems. In 2005 European Conference on Power Electronics and Applications. IEEE.

  • Fatemi SM, Shadlu MS and Talebkhah A (2020) A New Method for maximum power point tracking in solar PV systems by combining extremum seeking method (ESM) and Model predictive control (MPC). In 2020 11th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), pp 1–5

  • Ishaque K, Salam Z (2012) A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition. IEEE Trans Ind Electron 60(8):3195–3206

    Google Scholar 

  • Ishaque K et al (2012) An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE Trans Power Electron 27(8):3627–3638

    Article  Google Scholar 

  • Ji Y-H et al (2010) A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions. IEEE Trans Power Electron 26(4):1001–1009

    Article  Google Scholar 

  • Koad RB, Zobaa AF, El-Shahat A (2016) A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Trans Sustain Energy 8(2):468–476

    Article  Google Scholar 

  • Lee S (2014) A three-phase grid-connected pv generation system with a constant voltage based maximum power point tracking. In: Juang J, Chen CY, Yang CF (Eds). Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES2013). Lecture Notes in Electrical Engineering, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-319-04573-3_68.

  • Li X, Wen H, Hu Y, Du Y, Yang Y (2021) A comparative study on photovoltaic MPPT algorithms under EN50530 dynamic test procedure. IEEE Trans Power Electron 36(4):4153–4168. https://doi.org/10.1109/TPEL.2020.3024211

    Article  Google Scholar 

  • Lian KL, Andrean V (2017) A new MPPT method for partially shaded PV system by combining modified INC and simulated annealing algorithm. Int Conf High Voltage Eng Power Syst (ICHVEPS) 2017:388–393

    Article  Google Scholar 

  • Lian K, Jhang J, Tian I (2014) A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization. IEEE J Photovolt 4(2):626–633

    Article  Google Scholar 

  • Liu F et al (2008) A variable step size INC MPPT method for PV systems. IEEE Trans Ind Electron 55(7):2622–2628

    Article  Google Scholar 

  • Liu Y-H et al (2012) A particle swarm optimization-based maximum power point tracking algorithm for PV systems operating under partially shaded conditions. IEEE Trans Energy Convers 27(4):1027–1035

    Article  Google Scholar 

  • Messalti S, Harrag A, Loukriz A (2017) A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation. Renew Sustain Energy Rev 68:221–233

    Article  Google Scholar 

  • Motahhir S et al. (2018) Modeling of photovoltaic system with modified incremental conductance algorithm for fast changes of irradiance. International Journal of Photoenergy, 2018

  • Mousa HH, Youssef A-R, Mohamed EE (2019) Variable step size P&O MPPT algorithm for optimal power extraction of multi-phase PMSG based wind generation system. Int J Electr Power Energy Syst 108:218–231

    Article  Google Scholar 

  • Munir MI, Aldhanhani T, and Al Hosani KH (2017) Control of grid connected pv array using P&O MPPT algorithm. In 2017 Ninth Annual IEEE Green Technologies Conference (GreenTech). IEEE

  • Oshaba A, Ali E, Elazim SA (2017) PI controller design via ABC algorithm for MPPT of PV system supplying DC motor–pump load. Electr Eng 99(2):505–518

    Article  Google Scholar 

  • Owusu-Nyarko I, Elgenedy MA, and Ahmed K (2019) Combined temperature and irradiation effects on the open circuit voltage and short circuit current constants for enhancing their related PV-MPPT algorithms. In 2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE). 2019. IEEE.

  • Phimmasone V et al. (2009) Improvement of the maximum power point tracker for photovoltaic generators with particle swarm optimization technique by adding repulsive force among agents. In 2009 International Conference on Electrical Machines and Systems. IEEE.

  • Ram JP, Babu TS, Rajasekar N (2017) A comprehensive review on solar PV maximum power point tracking techniques. Renew Sustain Energy Rev 67:826–847

    Article  Google Scholar 

  • Renaudineau H et al (2014) A PSO-based global MPPT technique for distributed PV power generation. IEEE Trans Ind Electron 62(2):1047–1058

    Article  Google Scholar 

  • Rifa’i M and Ratnaika P (2012) Pemodelan dan analisis panel photovoltaik. In Conference Informatic, Telecommunications Electrical Engineering. 2012.

  • Safari A, Mekhilef S (2010) Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Trans Ind Electron 58(4):1154–1161

    Article  Google Scholar 

  • Scaria R, Neela R and Jos BM (2021) Particle swarm optimizer based maximum power point controller for the photovoltaic system. 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2021, pp. 410–417,https://doi.org/10.1109/ICICT50816.2021.9358715.

  • Shi Y and Eberhart RC (1998) Parameter selection in particle swarm optimization. In International Conference on Evolutionary Programming. Springer

  • Soon TK, Mekhilef S (2014) A fast-converging MPPT technique for photovoltaic system under fast-varying solar irradiation and load resistance. IEEE Trans Ind Informatics 11(1):176–186

    Article  Google Scholar 

  • Tey KS, Mekhilef S (2014) Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Sol Energy 101:333–342

    Article  Google Scholar 

  • Trelea IC (2003) (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325

    Article  MathSciNet  Google Scholar 

  • Zeddini MA et al (2016) PSO-based MPPT control of wind-driven self-excited induction generator for pumping system. Renew Energy 95:162–177

    Article  Google Scholar 

  • Zhu W et al (2018) ’Modified hill climbing MPPT algorithm with reduced steady-state oscillation and improved tracking efficiency’. J Eng 2018(17):1878–1883

    Article  Google Scholar 

Download references

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Correspondence to R. Mirzaei.

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Sheikh Ahmadi, S.H., Karami, M., Gholami, M. et al. Improving MPPT Performance in PV Systems Based on Integrating the Incremental Conductance and Particle Swarm Optimization Methods. Iran J Sci Technol Trans Electr Eng 46, 27–39 (2022). https://doi.org/10.1007/s40998-021-00459-0

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