IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipas036054422202374x.html
   My bibliography  Save this article

An efficient AOA-RERNN control approach for a non-isolated quasi-Z-source novel multilevel inverter based grid connected PV system

Author

Listed:
  • Santhi, R.
  • Srinivasan, A.

Abstract

In this paper, an effective control strategy based non-isolated Quasi-Z-Source (QZS) novel multilevel inverter topology (NIQZS-NMLI) is proposed for interfacing photovoltaic (PV) system. The proposed approach is the combination of Archimedes optimization algorithm (AOA) and Recalling-Enhanced Recurrent Neural Network (RERNN) called AOA-RERNN approach. Here, the modelling design of non-isolated QZS-NMLI topology is developed with new storage devices to distribute the maximum power from the photovoltaic power generating system. This novel multilevel inverter topology reduced the number of switches and the total harmonic distortion of the system, and also it is used to achieve the higher boost capability, lesser voltage stress across the active switching devices, and greater modulation index for the inverter. The objective function is determined depending on its controller parameters with constraints, like voltages, current, power and modulation. These parameters have been employed as the inputs of AOA-RERNN approach. This AOA-RERNN approach increases the voltage profile, power supply and decreases the power oscillations when sharing the power to the load. The maximum power distribution is guaranteed to the load by RERNN depending on the extraction of maximum power from the PV source. The proposed approach is executed in MATLAB/Simulink site; its performance is analyzed with existing approaches.

Suggested Citation

  • Santhi, R. & Srinivasan, A., 2023. "An efficient AOA-RERNN control approach for a non-isolated quasi-Z-source novel multilevel inverter based grid connected PV system," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s036054422202374x
    DOI: 10.1016/j.energy.2022.125492
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422202374X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125492?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Han, Yongming & Liu, Shuang & Geng, Zhiqiang & Gu, Hengchang & Qu, Yixin, 2021. "Energy analysis and resources optimization of complex chemical processes: Evidence based on novel DEA cross-model," Energy, Elsevier, vol. 218(C).
    2. Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Veronese da Silva, Aline & Costa, Marcelo Azevedo & Lopes-Ahn, Ana Lúcia, 2022. "Accounting multiple environmental variables in DEA energy transmission benchmarking modelling: The 2019 Brazilian case," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    2. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
    3. Li, Yongqing & Ma, Huimin & Xiong, Jie & Zhang, Jinlong & Ponnamma Divakaran, Pradeep Kumar, 2022. "Manufacturing structure, transformation path, and performance evolution: An industrial network perspective," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    4. Rizwan Fazal & Syed Aziz Ur Rehman & Muhammad Ishaq Bhatti & Atiq Ur Rehman & Fariha Arooj & Umar Hayat, 2021. "A Cross-Sectoral Investigation of the Energy–Environment–Economy Causal Nexus in Pakistan: Policy Suggestions for Improved Energy Management," Energies, MDPI, vol. 14(17), pages 1-22, September.
    5. Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
    6. Liu, Jintao & Chen, Liangchao & Xu, Wei & Feng, Mingfei & Han, Yongming & Xia, Tao & Geng, Zhiqiang, 2023. "Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm," Energy, Elsevier, vol. 262(PB).
    7. Han, Yongming & Lou, Xiaoyi & Feng, Mingfei & Geng, Zhiqiang & Chen, Liangchao & Ping, Weiying & Lu, Gang, 2022. "Energy consumption analysis and saving of buildings based on static and dynamic input-output models," Energy, Elsevier, vol. 239(PC).
    8. Cong, Di & Liang, Lingling & Jing, Shaoxing & Han, Yongming & Geng, Zhiqiang & Chu, Chong, 2021. "Energy supply efficiency evaluation of integrated energy systems using novel SBM-DEA integrating Monte Carlo," Energy, Elsevier, vol. 231(C).
    9. Yang, Yiran & Li, Gang & Luo, Tao & Al-Bahrani, Mohammed & Al-Ammar, Essam A. & Sillanpaa, Mika & Ali, Shafaqat & Leng, Xiujuan, 2023. "The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks," Energy, Elsevier, vol. 268(C).
    10. Josef Maroušek & Anna Maroušková, 2021. "Economic Considerations on Nutrient Utilization in Wastewater Management," Energies, MDPI, vol. 14(12), pages 1-16, June.
    11. Bienvenido-Huertas, David & Sánchez-García, Daniel & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2021. "Applying the mixed-mode with an adaptive approach to reduce the energy poverty in social dwellings: The case of Spain," Energy, Elsevier, vol. 237(C).
    12. Han, Yongming & Li, Jingze & Lou, Xiaoyi & Fan, Chenyu & Geng, Zhiqiang, 2022. "Energy saving of buildings for reducing carbon dioxide emissions using novel dendrite net integrated adaptive mean square gradient," Applied Energy, Elsevier, vol. 309(C).
    13. Ziqi Yuan & Guozhu Jia, 2022. "Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing," Information Technology & Tourism, Springer, vol. 24(4), pages 547-580, December.
    14. Stelian Brad & Mircea Murar & Grigore Vlad & Emilia Brad & Mariuța Popanton, 2021. "Lifecycle Design of Disruptive SCADA Systems for Waste-Water Treatment Installations," Sustainability, MDPI, vol. 13(9), pages 1-26, April.
    15. Gangmin Weng & Yue Pan & Jianpu Li, 2021. "Study on the Influencing Factors and Acting Path of the Sustainable Development of Rural Tourism Based on EEAM-ISM Model," Sustainability, MDPI, vol. 13(10), pages 1-16, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:263:y:2023:i:pa:s036054422202374x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.