A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution

Jin, Yi; Hou, Wenhui; Li, Guiqiang; Chen, Xiao

doi:10.3390/en10040541

Cited by 30 publications

(15 citation statements)

References 23 publications

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“…Consequently, several optimization algorithms have been proposed in the literature. Particle swarm optimization (PSO), glowworm swarm optimization (GSO), grey wolf optimization (GWO), artificial bee colony (ABC), ant colony optimization (ACO), bat algorithm, firefly algorithm, simulated annealing (SA), flower pollination algorithm, and S‐Jaya algorithm are few to name. However, instead of utilising a complicated MPPT algorithm under PSC, the maximum obtainable direct current (DC) power from a PV array can also be enhanced by reconfiguring the connection of the PV modules.…”

Section: Introductionmentioning

confidence: 99%

A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

Jha

Triar

2019

Int Trans Electr Energ Syst

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Summary The objective of this paper is to compare the performances of different photovoltaic (PV) array configurations (series, parallel, series‐parallel, total‐cross‐tied, bridge‐linked, and honey comb) and proposed hybrid configurations (series parallel–total cross tied, bridge linked–total cross tied, honey comb–total cross tied, and bridge linked–honey comb) under artificial and realistic partial shading conditions. The performances of all these configurations have been compared on the basis of their maximum powers, fill factors, thermal voltages, and relative power losses. Different PV array configurations have also been compared on the basis of other significant factors (faults, limitations of specific parameters of the PV system, and cost). The single‐diode model of PV module is considered in this paper for modelling of PV module. The obtained results of different PV array configurations give beneficial knowledge regarding the performances of these configurations for several shading patterns and assist to choose the most satisfactory configuration for a particular shading pattern.

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Section: Introductionmentioning

confidence: 99%

A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

Jha

Triar

2019

Int Trans Electr Energ Syst

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“…The disadvantage of these approaches is they can only track a single maximum, which is absent when solar panels are partially shaded. If the P‐V (or P‐I) characteristic is not unimodal, these approaches could only effectively touch a local maximum …”

Section: Introductionmentioning

confidence: 99%

“…If the P-V (or P-I) characteristic is not unimodal, these approaches could only effectively touch a local maximum. 1,2 Swarm-based algorithms are extensively utilized in many research algorithms. The example of metaheuristic methods is swarm intelligence (SI) techniques which are most widely used in various applications.…”

mentioning

confidence: 99%

A fuzzy‐elephant herding optimization technique for maximum power point tracking in the hybrid wind‐solar system

Veeramanikandan¹,

Selvaperumal²

2019

Int Trans Electr Energ Syst

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Summary In recent years, the attention towards the utilization of solar and wind power systems are becoming more popular because of its nonpolluted and nondepleting nature. However, these power sources rely on environmental conditions for power generation. So, extracting maximum power available from the wind and solar systems is essential. Many of the existing maximum power point tracking (MPPT) techniques' performances are not convenient in the hybrid operation of wind and solar power systems. So, to provide an optimum MPPT operation, this paper proposes a fuzzy logic controll er (FLC)–based elephant herding optimization (EHO) technique for MPPT in the hybrid wind‐solar system. The proposed fuzzy‐EHO (FEHO) method has the tendency to track the maximum power under the partial shading condition of the photovoltaic (PV) system and low wind speed situations. The results are compared with the conventional MPPT methods, and it shows that the proposed optimization method efficiently tracks the maximum power point of the wind‐solar power systems even with the variations in the climatic conditions. MATLAB tool is used in the design and operation of the proposed method. Furthermore, the performance of the proposed system is tested with an experimental setup and provides better efficiency.

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“…Also, algorithms based on artificial intelligence techniques such as fuzzy logic [13][14][15][16][17][18][19] and neural networks [20][21][22] have been used, as well as the implementation of optimization algorithms such as glowworm swarm [23], ant colony [24,25] and bee colony [26][27][28]. These algorithms are part of soft computing techniques and have the advantage of being easily implemented using embedded systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based MPPT Controller for a PV System

Robles-Algarín¹,

Giraldo²,

Álvarez³

2017

Energies

124

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Abstract:The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O) algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

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A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution

Cited by 30 publications

References 23 publications

A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

A detailed comparative analysis of different photovoltaic array configurations under partial shading conditions

A fuzzy‐elephant herding optimization technique for maximum power point tracking in the hybrid wind‐solar system

Fuzzy Logic Based MPPT Controller for a PV System

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