Global Maximum Power Point Tracking for PV Array under Partial Shading using Cuckoo Search

Anand, Rahul; Darbha, Swaroop; Kumar, Bhavnesh

doi:10.1109/piicon49524.2020.9113004

Cited by 15 publications

(11 citation statements)

References 16 publications

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“…As a result, researchers have the opportunity to incorporate mechanisms into the algorithm as common software updates since the artifcially constructed machine has a programming program (frmware) that governs its behavior, which is easier than searching for a natural phenomenon to justify the change. A comparison between MPPT and the cuckoo search algorithm (CUSA) [42][43][44][45], a comparison between MPPT and particle swarm optimization (PSO), and a comparison between MPPT and grey wolf optimization are presented in order to demonstrate its viability under PSCS. Figure 6 displays the P-V curve and I-V curve for the diferent irradiance circumstances used in the investigation.…”

Section: Dso-based Mppt Approachmentioning

confidence: 99%

“…Consequently, tracking MPP in uniform irradiance and PSCS detection are the two main uses of P&O in their proposed convention [13]. Partial shading implementation of maximum power point tracking (MPPT) using cuckoo search is shown in [14]. Tis paper's research focuses on the 6 × 4 TCT confguration of PV arrays to maximise energy output regardless of the surrounding conditions.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing MPPT Control for Enhanced Efficiency in Sustainable Photovoltaic Microgrids: A DSO-Based Approach

Mazumdar,

Biswas,

Sain

et al. 2024

International Transactions on Electrical Energy Systems

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The output of photovoltaic (PV) systems is significantly impacted by the vagaries of ambient temperature, solar irradiance, and environmental fluctuations. To achieve the utmost attainable power from PV systems, it is desired to be efficient at the maximum power point in diverse weather climates. Maximum power point tracking (MPPT) is used to schedule a designated location from where the highest power can be harvested. In the context of solar photovoltaic systems connected with DC microgrid platforms, this study introduces a recently developed drone squadron optimization (DSO) scheme that tracks the global maximum power point under PSCS difficulties. Furthermore, an exhaustive comparative analysis has been presented among particle swarm optimization (PSO), cuckoo search algorithm (CUSA), and grey wolf optimization (GWO) under different operating environments to endorse the supremacy of the nominated technique. The suggested method performs noticeably faster than many other methods currently in use, and in addition to offering the highest power, it can also use bidirectional power flow regulation in both constant and variable air conditions. Lastly, an MPPT system interfaced with the DC microgrid based on DSO ensures a sustainable and reliable architecture to provide at load in low power generating situations.

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Section: Dso-based Mppt Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing MPPT Control for Enhanced Efficiency in Sustainable Photovoltaic Microgrids: A DSO-Based Approach

Mazumdar,

Biswas,

Sain

et al. 2024

International Transactions on Electrical Energy Systems

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“…One-diode or two-diode equivalent circuits are used to model the cell. In this study, the single diode circuit model given in Figure 1 For one-diode model, the output current is obtained with Kirchoff's current law given in equation ( 1) [15].…”

Section: Photovoltaic Systemmentioning

confidence: 99%

Comparison of Maximum Power Point Tracking Methods Using Metaheuristic Optimization Algorithms for Photovoltaic Systems

BİLGİN

Yazıcı

2021

Sakarya University Journal of Science

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The maximum power value that can be obtained from photovoltaic systems can change continuously due to environmental conditions such as temperature, sunlight and partial shading. Direct current-direct current (DC-DC) converters and maximum power point tracking (MPPT) algorithms are required, especially in cases of partial shading, in order for the photovoltaic systems to operate at the maximum power point, that is, to draw the maximum possible power value from the system. In this study, simulation studies has been carried out for two different partially shaded scenarios using the boost-type DC-DC converter and MPPT algorithm in the PV array consisting of 3 panels connected in series. In the simulation studies, the output powers obtained by the application of particle swarm optimization, cuckoo optimization, bat optimization and firefly optimization techniques as MPPT algorithm has been compared. In the scenarios examined, the firefly optimization algorithm reached the maximum power point faster, and it has been observed that the firefly optimization method obtained the highest average power at the end of the simulation periods.

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“…In recent years, to solve multiple peak values generated from the P-V curve due to certain modules in a PVMA being shaded, where conventional MPPT methods became invalid, many smart MPPT methods were proposed [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. These methods included the cuckoo search algorithm (CSA) [9][10][11], cat swarm optimization (CSO) [12][13][14], genetic algorithm (GA) [15][16][17], teaching-learning-based optimization (TLBO) [18], grey wolf algorithm [19,20] and bat algorithm (BA) [21][22][23]; however, each algorithm had its unique strengths and constraints. Among them, CSA [9][10][11] was a global search algorithm inspired by bird behavior of food searching.…”

Section: Introductionmentioning

confidence: 99%

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

Chao,

Bau

2024

Electronics

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In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses.

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Global Maximum Power Point Tracking for PV Array under Partial Shading using Cuckoo Search

Cited by 15 publications

References 16 publications

Optimizing MPPT Control for Enhanced Efficiency in Sustainable Photovoltaic Microgrids: A DSO-Based Approach

Optimizing MPPT Control for Enhanced Efficiency in Sustainable Photovoltaic Microgrids: A DSO-Based Approach

Comparison of Maximum Power Point Tracking Methods Using Metaheuristic Optimization Algorithms for Photovoltaic Systems

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

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