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Optimized Control Strategy for Photovoltaic Hydrogen Generation System with Particle Swarm Algorithm

Author

Listed:
  • Hongyang He

    (Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, China)

  • Zhigang Lu

    (Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, China)

  • Xiaoqiang Guo

    (Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, China)

  • Changli Shi

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Dongqiang Jia

    (State Grid Beijing Electric Power Company, Beijing 100031, China)

  • Chao Chen

    (State Grid Jiaxing Electric Power Supply Company, Jiaxing 314033, China)

  • Josep M. Guerrero

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

Suggested Citation

  • Hongyang He & Zhigang Lu & Xiaoqiang Guo & Changli Shi & Dongqiang Jia & Chao Chen & Josep M. Guerrero, 2022. "Optimized Control Strategy for Photovoltaic Hydrogen Generation System with Particle Swarm Algorithm," Energies, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1472-:d:751408
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    References listed on IDEAS

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    Cited by:

    1. Kamalakannan, N. & Vinothkumar, M., 2024. "Marine predators algorithm with deep learning based solar photovoltaic system modelling and optimization of green hydrogen production," Renewable Energy, Elsevier, vol. 232(C).
    2. Sy Ngo & Chian-Song Chiu & Thanh-Dong Ngo, 2022. "A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems," Energies, MDPI, vol. 15(20), pages 1-18, October.
    3. Yukun Dong & Yu Zhang & Fubin Liu & Zhengjun Zhu, 2022. "Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 15(8), pages 1-18, April.

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