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A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters

Author

Listed:
  • Lei Song

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Lijun Huang

    (Guangzhou Haige Communications Group Incorporated Company, Guangzhou 510700, China)

  • Bo Long

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Fusheng Li

    (School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract

Transformerless grid-connected inverters are of great industrial value in photovoltaic power generation. However, the direct current (DC) induced into the inverter’s output degrades the power quality of the grid. Recently, a back-propagation neural work proportional–integral–derivative (BP-PID) scheme has proven helpful in solving this problem. However, this scheme can be improved by reducing the suppressing time and overshoot. A genetic algorithm (GA)-based DC current minimization scheme, namely the genetic-algorithm-based BP-PID (GA-BP-PID) scheme, was established in this study. In this scheme, GA was used off-line to optimize the initial weights within the BP neural network. Subsequently, the optimal weight was applied to the online DC current suppression process. Compared with the BP-PID scheme, the proposed scheme can reduce the suppressing time by 59% and restrain the overshoot. A prototype of the proposed scheme was implemented and tested on experimental hardware as a proof of concept. The results of the scheme were verified using a three-phase inverter experiment. The novel GA-PB-PID scheme proposed in this study was proven efficient in reducing the suppressing time and overshoot.

Suggested Citation

  • Lei Song & Lijun Huang & Bo Long & Fusheng Li, 2020. "A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters," Energies, MDPI, vol. 13(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:746-:d:318183
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    References listed on IDEAS

    as
    1. Long Bo & Lijun Huang & Yufei Dai & Youliang Lu & Kil To Chong, 2018. "Mitigation of DC Components Using Adaptive BP-PID Control in Transformless Three-Phase Grid-Connected Inverters," Energies, MDPI, vol. 11(8), pages 1-22, August.
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    Cited by:

    1. Mukul Chankaya & Ikhlaq Hussain & Aijaz Ahmad & Irfan Khan & S.M. Muyeen, 2021. "Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System," Energies, MDPI, vol. 14(5), pages 1-22, March.
    2. Xiongchao Lin & Wenshuai Xi & Jinze Dai & Caihong Wang & Yonggang Wang, 2020. "Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes," Energies, MDPI, vol. 13(19), pages 1-18, October.
    3. Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of the Control of a Grid Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(7), pages 1-32, March.
    4. Boscaino, Valeria & Ditta, Vito & Marsala, Giuseppe & Panzavecchia, Nicola & Tinè, Giovanni & Cosentino, Valentina & Cataliotti, Antonio & Di Cara, Dario, 2024. "Grid-connected photovoltaic inverters: Grid codes, topologies and control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    5. Marcel Nicola & Claudiu-Ionel Nicola, 2021. "Fractional-Order Control of Grid-Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers," Energies, MDPI, vol. 14(2), pages 1-25, January.

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