Detection method of island blind spot of grid-connected photovoltaic system based on improved artificial fish swarm algorithm

Jiahao Zhang; Wenzhen Wu; Shiwei Zhu

doi:10.1117/12.2656484

20 October 2022 Detection method of island blind spot of grid-connected photovoltaic system based on improved artificial fish swarm algorithm

Jiahao Zhang, Wenzhen Wu, Shiwei Zhu

Author Affiliations +

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245142 (2022) https://doi.org/10.1117/12.2656484
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Popular island detection methods generally include active detection methods, passive detection methods and communication detection methods. The active detection method will cause instability in the power quality of the power grid due to the addition of other parameters, and the passive detection method will cause a large detection blind area because the threshold is difficult to determine, resulting in inaccurate island detection results, and the islanding phenomenon may cause serious harm to the power grid and related personnel when it occurs. Optimization problem has always been a hot problem in scientific research, because the traditional optimization scheme has many defects in solving problems such as large dimensionality and multimodality, this paper aims to study the artificial fish swarm algorithm, and combine the advantages of various algorithms, and proposes a grid-connected photovoltaic system island detection method based on the improved artificial fish swarm algorithm. Using the optimization principle of artificial fish population to extract the relevant electrical characteristics, the large number of data sets are placed in the algorithm environment, matlab/Simulink and Python are used to establish a photovoltaic system grid-connected model, simulation and verification are carried out and the experimental results are compared with common intelligent detection methods, and the results show that in the algorithm environment, the blind area of island detection is small.

Citation Download Citation

Jiahao Zhang, Wenzhen Wu, and Shiwei Zhu "Detection method of island blind spot of grid-connected photovoltaic system based on improved artificial fish swarm algorithm", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245142 (20 October 2022); https://doi.org/10.1117/12.2656484

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KEYWORDS

Detection and tracking algorithms

Particles

Photovoltaic detectors

Data modeling

Distributed computing

Evolutionary algorithms

Optimization (mathematics)

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