PriMera Scientific Engineering (ISSN: 2834-2550)

Research Article

Volume 5 Issue 3

Design Study of high-power PV grid-connected Inverter System based on the Particle Swarm Algorithm

Qinghe Xu, Noppadol Amdee* and Adisak Sangsongfa

August 08, 2024

DOI : 10.56831/PSEN-05-150

Abstract

Photovoltaic (PV) power generation is an essential form of renewable energy. A grid-connected PV inverter is the core equipment of a grid-connected PV power generation system. Based on the working principle of a high-power PV grid-connected inverter, the design of a 500 kW PV grid-connected inverter system is considered as an example. The equipment selection and parameter design methods of critical components, such as DC support capacitors, DC to AC modules, inductors, and capacitors, are introduced, and the overall system control strategy scheme and maximum power point tracking strategy are proposed. The results of MATLAB system simulation and field measurement experiments show that the control system can ensure that the output three-phase voltage and current are always in the same phase and frequency and that the output power is stable, fully meeting the grid connection requirements. In addition, the system has high conversion efficiency, good harmonic suppression, and a good MPPT tracking effect based on the particle swarm algorithm, which has high application and promotion value.

Keywords: Grid-connected inverter; Harmonic; System Design; MPPT; Conversion efficiency; Particle swarm algorithm

References

  1. Ding K., et al. “A novel single-phase asymmetric 5-level inverter”. Proceedings of The CSEE 11 (2004): 116-120.
  2. Guo LL., et al. “Nonparametric sliding mode predictive control strategy for a three-phase LCL grid-connected inverter”. Power System Protection and Control 50 (2022): 72-82.
  3. Qiu JL., et al. “Leakage current suppression and balance control of neutral point potential for three-level transformerless inverter”. Automation of Electric Power Systems 45 (2021): 161-170.
  4. Liang CS. “Research on the Selection of Centralized Inverter and String Inverter in Large-scale Photovoltaic Power Station”. Telecom Power Technology 37 (2020): 283-285.
  5. Zhang X., et al. “Overview of high efficiency photovoltaic inverter”. Power Technology 40 (2016): 931-934.
  6. Prasad VH. “Analysis and comparison of space vector modulation schemes for three-leg and four-leg voltage source inverters”. Master dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA (1997).
  7. Qiu XN, Xu QH and Zhang XJ. “Design of 100kW photovoltaic grid connected inverter system”. Control Engineering 20 (2013): 72-75.
  8. Wang TCY., et al. “Output filter design for a grid-interconnected three-phase inverter”. Power Electronics Specialist Conference (2003): 779-784.
  9. Xu DH. “Modeling and control of power electronics system”. Beijing, China Machine Press (2005).
  10. Chen K, Yang Y and Shang JP. “Research on control strategy of grid-connected inverter in photovoltaic power system”. Process Automation Instrumentation 41 (2020): 46-50.
  11. Zhao H and Hu RJ. “Space-Vector Pulse Width Modulation and it’s Simulation Based on Simulink”. Transactions of China Electrotechnical Society 30 (2015): 350-353.
  12. Gao Z., et al. “A carrier based SVPWM Begins with Zero Voltage for Three-Level Neutral Point Clamped Converter”. Transactions of China Electrotechnical Society 35 (2020): 2194-2205.
  13. Tang JZ, Wang CL and Fang XF. “MPPT implementation strategy based on the conductance increment method”. Power Electronics 45 (2011): 73-75.
  14. Ni Y and Hao SX. “Motion characteristics analysis of P&Q control MPPT system”. Acta Electronica Sinica 43 (2015): 1388-1394.
  15. Kou GY and Wei GH. “Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization”. International Journal of Advanced Computer Science and Applications 14 (2023): 982-991.
  16. Lu HX., et al. “A particle swarm optimization algorithm based on deep deterministic policy gradient”. Journal of University of Electronic Science and Technology of China 50 (2021): 199-206.
  17. Rauf HT., et al. “Particle Swarm Optimization with Probability Sequence for Global Optimization”. IEEE Access 8 (2020): 110535-110549.
  18. Guo L and Abdul NMM. “Design and Evaluation of Fuzzy Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking on Photovoltaic System Under Partial Shading Conditions”. Frontiers in Energy Research 9 (2021): 712175.
  19. Li Z., et al. “Study of photovoltaic multi-mode maximum power point tracking based on improved quantum particle swarm algorithm”. Acta Energiae Solaris Sinica 42 (2021): 221-229.
  20. Zhang Y., et al. “Photovoltaic MPPT algorithm based on adaptive particle swarm optimization neural-fuzzy control”. Journal of Intelligent and Fuzzy Systems 44 (2023): 341-351.
  21. Zhao B., et al. “Multi-peak MPPT control of PV array based on improved ALO algorithm”. Acta Energiae Solaris Sinica 42 (2021): 132-139.
  22. Zheng LW, Liu SR and Xu QH. “Constant power flow control at the grid-connected point of photovoltaic microgrid based on nonlinear diffusion particle swarm optimization”. Power System Technology 34 (2010): 152-156.
  23. Xu QH, Wu TH and Wang WW. “Nonlinear dissipative particle swarm algorithm and its applications”. IEEE ACCESS 9 (2021): 158862-158871.
  24. Kennedy J and Eberhart RC. “Particle swarm optimization”. Proceedings of the IEEE International Conference on Neural Networks (1995): 1942-1948.
  25. Shi Y and Eberhart R. “Empirical study of particle swarm optimization”. International Conference on Evolutionary Computation (1999): 1945-1950.