Improved Invasive Weed Optimization Algorithm for Global Maximum Power Point Tracking of PV Array Under Partial Shading Conditions
International Journal of Applied Metaheuristic Computing • 2022
معلومات البحث
المؤلفون
Hegazy Zaher; Mohamed Husien Mohamed Eid; Radwa S. A. Gad; I. M. Abdelqawee
الكلمات المفتاحية
Global Maximum Power Point Tracking; Improved Invasive Weed; Modern Optimization; Partial Shading; PV
Systems
المجلة العلمية
International Journal of Applied Metaheuristic Computing
الناشر
Not Available
المجلد
13
العدد
1
الصفحات
21
publication.type
International
رابط البحث
Not Available
المواد المرفقة
Not Available
الملخص
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 and 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.
(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 and 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.
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