Enhanced energy output from a PV system under partial shaded conditions through grey wolf optimizer

Motahhir, Saad; Chtita, Smail; Chouder, Aissa; Hammoumi, Aboubakr El

doi:10.1016/j.clet.2022.100533

Cited by 23 publications

(4 citation statements)

References 21 publications

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“…However, their performance under PSC is often limited by the inability to consistently identify the GMPP, leading to suboptimal power extraction from PV arrays [5,6]. Recent advancements in MPPT technologies have introduced several algorithms to address the complexities introduced by PSCs, including Fuzzy Logic Control (FLC) [7,8], Grey Wolf Optimization (GWO) [9,10], Particle Swarm Optimization (PSO) [11][12][13], and Artificial Neural Networks (ANNs) [14][15][16][17]. While the discussed MPPT algorithms are well-documented in the literature, there is a noticeable lack of diversity in ANN approaches, with many studies following similar methodologies or hybrids.…”

Section: Introductionmentioning

confidence: 99%

Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions

Sørensen,

Mihet-Popa

2024

Preprint

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The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in MATLAB/SIMULINK evaluated these algorithms’ performance under various shading scenarios. The results indicate that while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. The study concludes that integrating AI and ML into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and support the green energy transition.

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Section: Introductionmentioning

confidence: 99%

Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions

Sørensen,

Mihet-Popa

2024

Preprint

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“…A multitude of MPPT control techniques, which make use of swarm optimization, are proposed as a means of enhancing the efficiency of solar energy utilization in PV systems when PSC is employed [8,9]. Among the most commonly utilized intelligent MPPT methodologies are PSO [10], GWO [11], the multiverse algorithm [12], the cuckoo search algorithm [13], black widow optimization [14], and the whale optimization algorithm [15], etc. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

Li,

He,

2024

Energies

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In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.

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“…However, the approach does have a drawback in that it requires significant time delays for the particles to gather towards the MPP, resulting in longer computation times 44 . References 45 , 46 propose a MPPT technique which uses Gray Wolf Optimization (GWO). Drawing inspiration from grey wolves, this approach mimics the leadership hierarchy and hunting procedure observed in wildlife.…”

Section: Introductionmentioning

confidence: 99%

Investigation of single and multiple MPPT structures of solar PV-system under partial shading conditions considering direct duty-cycle controller

Youssef,

Hefny,

Ali

2023

Sci Rep

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Partial shading of solar panels diminishes their operating efficiency and energy synthesized as it disrupts the uniform absorption of sunlight. To tackle the issue of partial shading in photovoltaic (PV) systems, this article puts forward a comprehensive control strategy that takes into account a range of contributing factors. The proposed control approach is based on using multi-string PV system configuration in place of a central-type PV inverter for all PV modules with a single DC-DC converter. This adaptation enhances overall efficiency across varying radiation levels. Also, the proposed technique minimizes the overall system cost by reducing the required sensors number by utilizing a radiation estimation strategy. The converter switching strategy is synthesized considering direct duty-cycle control method to establish the maximum power point (MPP) location on the P–V curve. The direct duty-cycle tracking approach simplifies the control system and improves the system’s response during sudden partial shading restrictions. To validate the effectiveness of the suggested MPPT method, two system configurations were constructed using MATLAB/SIMULINK software and assessed under various partial shading scenarios. Additionally, a multi-string system was subjected to real irradiance conditions. The sensor-less MPPT algorithm proposed achieved an impressive system efficiency of 99.81% with a peak-to-peak ripple voltage of 1.3V. This solution offers clear advantages over alternative approaches by reducing tracking time and enhancing system efficiency. The system findings undoubtedly support the theoretical scrutiny of the intended technique.

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Enhanced energy output from a PV system under partial shaded conditions through grey wolf optimizer

Cited by 23 publications

References 21 publications

Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions

Development and Comparative Evaluation of Traditional and Advanced MPPT Algorithms for PV Systems in Partial Shading Conditions

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

Investigation of single and multiple MPPT structures of solar PV-system under partial shading conditions considering direct duty-cycle controller

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