DMPPT Control of Photovoltaic Microgrid Based on Improved Sparrow Search Algorithm

Yuan, Jianhua; Zhao, Ziwei; Ya-Ping, Liu; He, Bin; Wang, Lin; Xie, Binbin; Gao, Yanling

doi:10.1109/access.2021.3052960

Cited by 113 publications

(45 citation statements)

References 17 publications

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“…After a round of position updating of the whole sparrow population, T-distribution variation was carried out for some sparrows with better fitness based on sparrow x old i,j with better fitness. The variation formula was as follows: (12).…”

Section: B T-distribution Variationmentioning

confidence: 99%

“…Compared with other intelligent optimization algorithms, SSA has the characteristics of high search accuracy, fast convergence speed, good stability and strong robustness [11]. However, SSA is extremely prone to local convergence and convergence stagnation in the later period of convergence [12]. These problems will directly affect the optimization effect of SSA, resulting in the failure to find the global optimal solution.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the simulated annealing algorithm is used to determine the optimal solution. Literature [12] proposes an improved sparrow search algorithm (ISSA) sparrows, this method USES the center of gravity reverse learning mechanism to initialization of population, the population has better spatial distribution, and in the position of the discoverer update part introduces learning coefficient, improve the global search ability of the algorithm. At the same time, mutation operator is used to improve the sparrow position update formula, so as to avoid the local convergence problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

Liu

Wang

2021

IEEE Access

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Sparrow search algorithm (SSA) is easy to fall into local convergence and convergence stagnation. In order to solve these problems, this paper introduced Circle chaos map into the original SSA to improve its global search ability at the beginning of iteration. Meanwhile, it introduced T-distribution variation to affect the sparrow population position update rules in different iteration periods. Finally, we constructed the ''similarity function'' to measure the ''dispersion'' of the sparrow population, and formulated the search rules of the sparrow population under different ''dispersion''. In order to test the specific optimization performance of the proposed algorithm, the test results of 54 test functions are compared with those of 9 other algorithms which are widely used, and then the test results are analyzed using non-parametric tests in statistics. At the same time, this paper introduces this algorithm into three concrete engineering test problems for testing. The results of these tests all prove that the proposed algorithm has stronger global optimization ability and higher convergence precision compared with other algorithms.

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Section: B T-distribution Variationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

Liu

Wang

2021

IEEE Access

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“…Kumaravel and Ponnusamy improved the control parameters of the power controller based on SSA to optimize the power flow management of the smart grid system, which realized the real-time energy management in the microgrid [27]. In [28], the improved model based on SSA can track the distributed maximum power point more accurately and quickly and has good robustness, thus effectively solving the problem of power mismatch loss in a photovoltaic microgrid system. Zhu and Yousefi introduced an adaptive strategy on the basis of SSA and applied it to the optimization of proton exchange membrane fuel cell (PEMFC) stack model parameters.…”

Section: Introductionmentioning

confidence: 99%

A Novel Modified Sparrow Search Algorithm with Application in Side Lobe Level Reduction of Linear Antenna Array

Liang

Bin

et al. 2021

Wireless Communications and Mobile Computing

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Antenna arrays play an increasingly important role in modern wireless communication systems. However, how to effectively suppress and optimize the side lobe level (SLL) of antenna arrays is critical for communication performance and communication capabilities. To solve the antenna array optimization problem, a new intelligent optimization algorithm called sparrow search algorithm (SSA) and its modification are applied to the electromagnetics and antenna community for the first time in this paper. Firstly, aimed at the shortcomings of SSA, such as being easy to fall into local optimum and limited convergence speed, a novel modified algorithm combining a homogeneous chaotic system, adaptive inertia weight, and improved boundary constraint is proposed. Secondly, three types of benchmark test functions are calculated to verify the effectiveness of the modified algorithm. Then, the element positions and excitation amplitudes of three different design examples of the linear antenna array (LAA) are optimized. The numerical results indicate that, compared with the other six algorithms, the modified algorithm has more advantages in terms of convergence accuracy, convergence speed, and stability, whether it is calculating the benchmark test functions or reducing the maximum SLL of the LAA. Finally, the electromagnetic (EM) simulation results obtained by FEKO also show that it can achieve a satisfactory beam pattern performance in practical arrays.

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“…Liu et al [23] introduced chaos strategy into SSA and used adaptive inertia weight to balance the convergence speed and exploration ability of the algorithm, but between the speed of convergence and the ability of exploration, a certain part must be sacrificed. Yuan et al [24] used gravity center reverse learning mechanism to initialize the population so that the population has better spatial solution distribution. Secondly, the learning coefficient was introduced into the location updating part of the discoverer to improve the global search ability of the algorithm, but the local search ability of SSA was ignored.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Crossover Mutation Sparrow Search Algorithm

Tang

Song

et al. 2021

Mathematical Problems in Engineering

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Aiming at the inherent problems of swarm intelligence algorithm, such as falling into local extremum in early stage and low precision in later stage, this paper proposes an improved sparrow search algorithm (ISSA). Firstly, we introduce the idea of flight behavior in the bird swarm algorithm into SSA to keep the diversity of the population and reduce the probability of falling into local optimum; Secondly, we creatively introduce the idea of crossover and mutation in genetic algorithm into SSA to get better next-generation population. These two improvements not only keep the diversity of the population at all times but also make up for the defect that the sparrow search algorithm is easy to fall into local optimum at the end of the iteration. The optimization ability of the improved SSA is greatly improved.

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DMPPT Control of Photovoltaic Microgrid Based on Improved Sparrow Search Algorithm

Cited by 113 publications

References 17 publications

A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

A Novel Modified Sparrow Search Algorithm with Application in Side Lobe Level Reduction of Linear Antenna Array

A Fusion Crossover Mutation Sparrow Search Algorithm

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