A Maximum Power Point Tracking Method Based on a Modified Grasshopper Algorithm Combined with Incremental Conductance

The partial shading of photovoltaic (PV) modules due to clouds or blocking objects, such as buildings or tree leaves, is a common problem for photovoltaic systems. To address this, maximum power point tracking (MPPT) is implemented to find the global maximum power point (GMPP). In this paper, a new...

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Bibliographic Details
Published in:Energies (Basel) 2020-09, Vol.13 (17), p.4329
Main Authors: Wijaya, Bustani Hadi, Subroto, Ramadhani Kurniawan, Lian, Kuo Lung, Hariyanto, Nanang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alternative energy sources
Conductance
Control algorithms
Efficiency
Electricity
Fuzzy logic
grasshopper optimization algorithm
Heuristic
Incremental conductance
maximum power point tracking
Methods
Neural networks
Optimization
Optimization algorithms
Photovoltaics
PV system
Random variables
Renewable resources
Shading
Solar energy
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Summary:The partial shading of photovoltaic (PV) modules due to clouds or blocking objects, such as buildings or tree leaves, is a common problem for photovoltaic systems. To address this, maximum power point tracking (MPPT) is implemented to find the global maximum power point (GMPP). In this paper, a new hybrid MPPT is proposed that combines a modified grasshopper optimization algorithm (GOA) with incremental conductance (IC). In the first stage, the proposed modified GOA is implemented to find a suitable tracking area where the GMPP is located. Then the system moves to the second stage by implementing IC to get the correct GMPP. IC is a fast-performing and reliable algorithm. By combining GOA and IC, the proposed method can find the GMPP accurately with a short tracking time. Various experimental results show that the proposed method yields the highest tracking efficiency and lowest tracking time compared to some of the state-of-the-art MPPT algorithms, such as particle swarm and modified firefly optimizations.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13174329