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A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems

Author

Listed:
  • Ehtisham Lodhi

    (The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Fei-Yue Wang

    (Beijing Engineering Research Center of Intelligent Systems and Technology, Chinese Academy of Sciences, Beijing 100190, China)

  • Gang Xiong

    (The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    The Cloud Computing Center, Chinese Academy of Sciences, Dongguan 523808, China)

  • Ghulam Ali Mallah

    (Department of Computer Science, Shah Abdul Latif University, Khairpur 66111, Pakistan)

  • Muhammad Yaqoob Javed

    (Department of Electrical & Computer Engineering, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan)

  • Tariku Sinshaw Tamir

    (The SKL for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China)

  • David Wenzhong Gao

    (Department of Electrical & Computer Engineering, University of Denver, Denver, CO 80208, USA)

Abstract

Currently, grid-connected Photovoltaic (PV) systems are widely encouraged to meet increasing energy demands. However, there are many urgent issues to tackle that are associated with PV systems. Among them, partial shading is the most severe issue as it reduces efficiency. To achieve maximum power, PV system utilizes the maximum power point-tracking (MPPT) algorithms. This paper proposed a two-level converter system for optimizing the PV power and injecting that power into the grid network. The boost converter is used to regulate the MPPT algorithm. To make the grid-tied PV system operate under non-uniform weather conditions, dragonfly optimization algorithm (DOA)-based MPPT was put forward and applied due to its ability to trace the global peak and its higher efficiency and shorter response time. Furthermore, in order to validate the overall performance of the proposed technique, comparative analysis of DOA with adaptive cuckoo search optimization (ACSO) algorithm, fruit fly optimization algorithm combined with general regression neural network (FFO-GRNN), improved particle swarm optimization (IPSO), and PSO and Perturb and Observe (P&O) algorithm were presented by using Matlab/Simulink. Subsequently, a voltage source inverter (VSI) was utilized to regulate the active and reactive power injected into the grid with high efficiency and minimum total harmonic distortion (THD). The instantaneous reactive power was adjusted to zero for maintaining the unity power factor. The results obtained through Matlab/Simulink demonstrated that power injected into the grid is approximately constant when using the DOA MPPT algorithm. Hence, the grid-tied PV system’s overall performance under partial shading was found to be highly satisfactory and acceptable.

Suggested Citation

  • Ehtisham Lodhi & Fei-Yue Wang & Gang Xiong & Ghulam Ali Mallah & Muhammad Yaqoob Javed & Tariku Sinshaw Tamir & David Wenzhong Gao, 2021. "A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10778-:d:645248
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    References listed on IDEAS

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    Cited by:

    1. Maytham N. Meqdad & Seifedine Kadry & Hafiz Tayyab Rauf, 2022. "Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors," Future Internet, MDPI, vol. 14(10), pages 1-16, October.
    2. Lina Wang & Ehtisham Lodhi & Pu Yang & Hongcheng Qiu & Waheed Ur Rehman & Zeeshan Lodhi & Tariku Sinshaw Tamir & M. Adil Khan, 2022. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems," Energies, MDPI, vol. 15(10), pages 1-16, May.

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