A grasshopper optimization algorithm aided maximum power point tracking for partially shaded photovoltaic systems

Sridhar, R.; Subramani, C.; Pathy, Somashree

doi:10.1016/j.compeleceng.2021.107124

Cited by 25 publications

(13 citation statements)

References 24 publications

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“…Fig 11b depicts power graphs for the proposed IGOA and GOA [ 35 ]. Six CENTSYS solar modules (40 W) are used in a series-parallel combination using the irradiance level [800 100 1000 500 400 1000] at 25°C having GP at 140.2 W. As can be observed, the proposed IGOA tracks the GP in 0.13 s as compared to 2.95 s for GOA which confirms the outperformance of the proposed method as it follows the GP 14 times quicker.…”

Section: Resultsmentioning

confidence: 99%

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An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

Wasim,

Amjad,

Abbasi

et al. 2023

PLoS ONE

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Global maximum power point (GMPP) tracking under shading conditions with low tracking time and reduced startup oscillations is one of the challenging tasks in photovoltaic (PV) systems. To cope with this challenge, an improved grasshopper optimization algorithm (IGOA) is proposed in this work to track the GMPP under partial shading conditions (PSC). The performance of the proposed approach is compared with well-known swarm intelligence techniques (SITs) such as gray wolf optimization (GWO), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), improved SSA based on PSO (ISSAPSO), and GOA in terms of tracking time, settling time, failure rate, and startup oscillations. For a fair comparison, the PV system is analysed under uniform irradiance and three PSCs having four to six peaks in the power-voltage characteristic curves and using three to six search agents for each SIT. For this purpose, a PV system containing six solar panels has been built using MATLAB/SIMULINK software, and statistical analysis is performed in detail. The results show that the IGOA tracks the GMPP in 0.07 s and settles the output in 0.12 s which is 25% to 96% faster than its counterparts. Moreover, IGOA proves its consistency with a minimal tracking failure rate of 0% for four to six search agents with negligible startup oscillations. This work is expected to be helpful to PV system installers in obtaining maximum benefits from the installed system.

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Section: Resultsmentioning

confidence: 99%

“…However, the authors discussed the proposed technique’s behavior for limited peaks without mentioning the swarm size. In 2021, a study was conducted to increase the GOA’s performance [ 35 ]. The proposed strategy was compared to P&O, PSO, and differential evolution in terms of power extraction and conversion efficiency.…”

Section: Introductionmentioning

confidence: 99%

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

Wasim,

Amjad,

Abbasi

et al. 2023

PLoS ONE

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show abstract

“…In [16], the optimal reconfiguration process of the partially shaded PV array has been solved based on GOA such that the solar radiation distributes in a regular form as much as possible. The efficiency of GOA to attain the operation point of a partially shaded PV system, by tuning the duty cycle of a DC-DC converter, has been evaluated in [39]. Figure 3 shows the diagram of the PV system.…”

Section: Goa Application For the Mpptmentioning

confidence: 99%

Maximum power point tracking in partially shaded photovoltaic systems using grasshopper optimization algorithm

Tabar

Shahnazari

Heshmati

2022

IET Renewable Power Gen

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The P-V curve of photovoltaic (PV) arrays has several peaks under partial shading conditions (PSCs). Therefore, to extract maximum power, it is necessary to detect its global maximum power point (GMPP). The conventional maximum power point tracking (MPPT) algorithms are trapped in local maximum power point (LMPP) under PSCs. In this paper, a MPPT method based on grasshopper optimization algorithm (GOA) has been presented. A PV system has been implemented in Matlab/Simulink and different operating conditions have been investigated to compare the performance of the proposed method with two well-known grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms. Furthermore, an experimental setup has been developed to verify the efficiency of the proposed MPPT method. The simulation and experimental results confirmed the speed and accuracy of convergence compared to GWO and PSO algorithms.

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“…The proposed algorithm was evaluated on the basis of performance tracking and the result was compared with three well-known algorithms like P&O, PSO, and GWO. A new Grasshopper Optimization Algorithm (GOA) that can extract the maximum power under difficult shading conditions has been proposed by Sridhar et al [8]. In order to eliminate the undesirable content lower-order harmonic in the cascaded H-Bridge multilevel inverter, Padmanaban et al [9] suggested a hybrid algorithm for solar PV systems based on Artificial Neural Network-Newton Raphson (ANN-NR).…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

Kumar

Chauhan²,

Pandey³

et al. 2023

Sustainability

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In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.

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A grasshopper optimization algorithm aided maximum power point tracking for partially shaded photovoltaic systems

Cited by 25 publications

References 24 publications

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

Maximum power point tracking in partially shaded photovoltaic systems using grasshopper optimization algorithm

A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

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