A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System Under Partial Shading Conditions

Mohanty, Satyajit; Subudhi, Bidyadhar; Ray, Pravat Kumar

doi:10.1109/tste.2015.2482120

Cited by 690 publications

(290 citation statements)

References 14 publications

Supporting

Mentioning

279

Contrasting

Unclassified

Order By: Relevance

“…Within the meta-heuristic algorithms, particle swarm optimization (PSO) is the most used algorithm in literature, thanks to its simple implementation and powerful behaviour at PSC. Other methods in this group are, simulated annealing [8], Grey-Wolf optimization [9], DEPSO [10], firefly colony [11] and artificial bee colony. All these algorithms are investigated for advantages and drawbacks in the following sub-sections.…”

Section: Meta-heuristicsmentioning

confidence: 99%

“…The Grey-Wolf optimization (GWO) method is presented in [9] as an algorithm that overcomes such problems as steady-state oscillations, lower tracking efficiency, which are encountered in P&O and PSO methods. The proposed method detects the shading pattern variations and is faster to converge to the global maximum, and has reduced steady-state oscillations.…”

Section: Meta-heuristicsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of MPPT Algorithms for Partial Shading Conditions

Erdem

2017

Acta Phys. Pol. A

View full text Add to dashboard Cite

Maximum power point tracker in a photovoltaic system allows to maximize the energy drawn from the connected photovoltaic modules. In the partial shade conditions there can be more than one maximum point in photovoltaic output power curve. The solution for this situation is a maximum power point tracker algorithm, which finds the global maximum. In literature, there is a large number of studies on maximum power point trackers. Therefore designers are drowning in a sea of knowledge. This study eliminates similar studies and classified them into groups, and at the end of the study a comparison table is given to guide the designers in the performance information of the selected studies. This study aims to guide the designers to make a sensible selection of a maximum power point tracker algorithm for partial shade conditions.

show abstract

Section: Meta-heuristicsmentioning

confidence: 99%

Section: Meta-heuristicsmentioning

confidence: 99%

A Review of MPPT Algorithms for Partial Shading Conditions

Erdem

2017

Acta Phys. Pol. A

View full text Add to dashboard Cite

show abstract

“…In the last decade, several researchers have compared various MPPT techniques [9,10], focusing on the P-V characteristics [11][12][13][14][15], models [16][17][18][19], and methods [20][21][22][23][24][25][26][27][28][29][30][31][32][33] to track the maximum power of PV modules/arrays under partial shading conditions (PSC). The research on the PV output characteristics is mainly focused on the analysis of failure, power loss, and voltage variations in the MPPT method under the PSC.…”

Section: Introductionmentioning

confidence: 99%

Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions

Chen¹,

Ma²,

Wu³

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

Abstract:The output-voltage-power curves of photovoltaic (PV) arrays exhibit complex multi-peak shapes when local shading occurs. The existing maximum power point tracking (MPPT) algorithms to solve this multi-peak problem do not consider the possibility of tracking failures due to the time of the irradiance change. In this study, first, the reason for the failure of the global MPPT (GMPPT) algorithm is analyzed based on the PV array mathematical model and its output characteristics under partial shading conditions; then, in order to estimate the MPP voltage, an artificial neural network (ANN) is trained using environmental information such as irradiance. A hybrid MPPT method using an augmented state feedback precise linearization (AFL) controller combined with an ANN is proposed to solve problems such as the shift of the static operating point of the DC/DC boost converter. Finally, numerical simulations are conducted to validate the proposed method and eliminate the possibility of MPPT failure. The proposed hybrid MPPT method is compared with the conventional perturb and observe (P & O) method and the improved P & O method through simulations. Using the proposed neural network and nonlinear control strategy, the MPP can be tracked rapidly, accurately, and statically, proving that the method is feasible and effective.

show abstract

“…Artificial neural network (ANN) and fuzzy logic controller (FLC) based MPPT algorithms are considered to be part of artificial intelligent (AI) techniques (Lin et al 2011, Khateb et al 2014. The MPPT algorithms based on nature inspired optimization techniques are genetic algorithm (Larbes et al 2009), particle swarm optimization technique (Liu et al 2012), ant colony optimization (Jianga et al 2013), artificial bee colony (Benyoucef et al 2015), and grey wolf optimization technique (Mohanty et al 2016). The P&O method is easier to implement, but this algorithm fails to track MPP and will result in oscillation at steady state point.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

Naick

Chatterjee

2017

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

Photovoltaic (PV) system is one of the reliable alternative sources of energy and its contribution in energy sector is growing rapidly. The performance of PV system depends upon the solar insolation, which will be varying throughout the day, season and year. The biggest challenge is to obtain the maximum power from PV array at varying insolation levels. The maximum power point tracking (MPPT) controller, in association with tracking algorithm will act as a principal element in driving the PV system at maximum power point (MPP). In this paper, the simulation model has been developed and the results were compared for perturb and observe, incremental conductance, extremum seeking control and fuzzy logic controller based MPPT algorithms at different irradiation levels on a 10 KW PV array. The results obtained were analysed in terms of convergence rate and their efficiency to track the MPP.Article History: Received 3rd Oct 2016; Received in revised form 6th January 2017; Accepted 10th February 2017; Available onlineHow to Cite This Article: Naick, B. K., Chatterjee, T. K. & Chatterjee, K. (2017) Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation. Int Journal of Renewable Energy Development, 6(1), 65-74.http://dx.doi.org/10.14710/ijred.6.1.65-74

show abstract

A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System Under Partial Shading Conditions

Cited by 690 publications

References 14 publications

A Review of MPPT Algorithms for Partial Shading Conditions

A Review of MPPT Algorithms for Partial Shading Conditions

Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions

Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

Contact Info

Product

Resources

About