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Improved Invasive Weed Optimization Algorithm for Global Maximum Power Point Tracking of PV Array Under Partial Shading Conditions

Author

Listed:
  • Hegazy Zaher

    (Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt)

  • Mohamed Husien Mohamed Eid

    (Shoubra Faculty of Engineering, Benha University, Egypt)

  • Radwa S. A. Gad

    (Shoubra Faculty of Engineering, Benha University, Egypt)

  • I. M. Abdelqawee

    (Shoubra Faculty of Engineering, Benha University, Egypt)

Abstract

Photovoltaic (PV) array under partial shading conditions (PSCs) has several maximum power points (MPPs) on the power-voltage curve of the PV array. These points; have a unique global peak (GP) and the others are local peaks (LPs). This paper aims to study an improved version of a heuristic optimization technique namely, Invasive Weed Optimization (IWO) to track the global maximum power point (GMPP) of a PV array which is an important issue. The proposed improved IWO (IIWO) algorithm modifies IWO to speed up the convergence and make the system more efficient. In addition to study the effect of changing input parameters of IIWO on its performance. An overall statistical evaluation of IIWO, with standard IWO and Particle Swarm Optimization (PSO) is executed under different shading conditions. The simulation results show that IIWO has faster and better convergence as it can reach the GMPP in less time compared with other techniques.

Suggested Citation

  • Hegazy Zaher & Mohamed Husien Mohamed Eid & Radwa S. A. Gad & I. M. Abdelqawee, 2022. "Improved Invasive Weed Optimization Algorithm for Global Maximum Power Point Tracking of PV Array Under Partial Shading Conditions," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-21, January.
    • Handle: RePEc:igg:jamc00:v:13:y:2022:i:1:p:1-21
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