Dr. Wenzhen Wu Profile

Dr. Wenzhen Wu

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Proceedings Article | 20 October 2022 Paper

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

Jiahao Zhang, Wenzhen Wu, Shiwei Zhu

Proceedings Volume 12451, 1245142 (2022) https://doi.org/10.1117/12.2656484

KEYWORDS: Detection and tracking algorithms, Particles, Photovoltaic detectors, Data modeling, Photovoltaics, Optimization (mathematics), Evolutionary algorithms, Distributed computing, Algorithm development, Visualization

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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.

Proceedings Article | 1 August 2022 Paper

Electric vehicle load forecasting based on improved neural network based on differential evolution algorithm

Shiwei Zhu, Wenzhen Wu, Jiahao Zhang, Na Li

Proceedings Volume 12257, 122570V (2022) https://doi.org/10.1117/12.2640366

KEYWORDS: Neural networks, Electrical engineering, Neurons

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Under the environment of energy crisis and green development, the development of electric vehicles has become inevitable. However, the charging load of electric vehicles has great uncertainty, which will have a certain impact on the security and stability of the power grid. Accurately predicting the charging load of electric vehicles is an effective method to avoid this problem. Considering the spatiotemporal characteristics of electric vehicle charging load, an electric vehicle load prediction method based on differential evolution algorithm improved BP neural network is proposed to complete the search of the weight space and network structure space of the neural network at the same time. The optimal network structure. The algorithm adopts the (1+1)-ES binary evolution strategy, uses a new network structure crossover and mutation method, and speeds up the search of the neural network model and the algorithm through the co-evolution of dual population structure and adaptive mutation rate strategies. Convergence improves the learning ability of the network and reduces the prediction error of the BP neural network model. The model and the BP neural network model are respectively used to predict the load of electric vehicles, and the comparison results prove the superiority of the proposed model.

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