An improved quantum particle swarm photovoltaic multi‐peak mPPT method combined with Lévy flight

Chen, Lei; Li, Zhijun; Zhang, Yinan; Zhang, Yi

doi:10.1002/ese3.790

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…However, to suit the PV module data in this study, numerous common mathematical model formulations, including Gaussian, exponential, and polynomial models, should be used first. In Chen et al, 34 a maximum power tracking controlling scheme of PV module has been presented where the Lévy flight tactic was integrated with quantum PSO to change the particles mutation equation and boost their diversity in the population.…”

Section: Introductionmentioning

confidence: 99%

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Shaheen

Elsayed

Ginidi

et al. 2022

Energy Science & Engineering

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In this paper, an enhanced social network search algorithm (ESNSA) has been proposed to model the solar photovoltaic (PV) modules accurately and efficiently. The proposed algorithm is introduced to minimize the least root-mean-square error (RMSE) between the calculated and experimental data for the single, double, and triple diode models of Kyocera KC200GT, STM6(40/36), and Photowatt-PWP201 modules. The original SNSA was inspired by users on social networks and their many moods, including imitation, conversation, disputation, and innovation mood. Two strategies are presented for the ESNSA. The first strategy is the powerful exploitation strategy (PES), which is intended to increase the SNSA's performance by boosting searching around the best view of all users. The second strategy is to suggest an adaptable parameter to aid in the exploitation of iterations in the end. Diverse comparisons and statistical analyses for validation purposes are carried out for mono-crystalline STM6(40/36), multicrystalline KC200GT, and polycrystalline photowatt-PWP201 modules. The comparative studies and statistical measures show the consistency and accurateness of the proposed ESNSA. As a numerical application, for the mono-crystalline STM6(40/36) PV module, the proposed ESNSA achieves the least RMSE of 1.751631E−3, 1.769953E−3, and 1.696504E−3, respectively for the three models. Also, it shows high robustness compared to the original SNSA as it acquires the least standard errors for the three models of 2.56E−18, 1.76E−6, and 1.24E−5, respectively. Moreover, the proposed ESNSA provides a higher convergence speed where it is approximately reached to the least RMSE in less than 60%, 50%, and 60% of the iterations for the three models, respectively. Nevertheless, the proposed ESNSA provides better performance than miscellaneous published approaches in minimizing the RMSE, with high robust indices.

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Section: Introductionmentioning

confidence: 99%

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Shaheen

Elsayed

Ginidi

et al. 2022

Energy Science & Engineering

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Neighborhood-search-based enhanced multi-strategy collaborative artificial Bee colony algorithm for constrained engineering optimization

Zhang

Yang

et al. 2023

Soft Comput

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Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

et al. 2022

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This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

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An improved quantum particle swarm photovoltaic multi‐peak mPPT method combined with Lévy flight

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 4 publications

References 45 publications

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Neighborhood-search-based enhanced multi-strategy collaborative artificial Bee colony algorithm for constrained engineering optimization

Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

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