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Optimization control technology of optical storage network inverter based on gazelle algorithm optimized convolutional neural network
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Xiulan Pang1, Xiaofeng Li1, Qi Yang1, Xian Li1, Xuehong Li1, Wenxing Jin2
Renewable Energy Resources | 2025, 43(5) : 687 - 695
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Renewable Energy Resources | 2025, 43(5): 687-695
Optimization control technology of optical storage network inverter based on gazelle algorithm optimized convolutional neural network
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Xiulan Pang1, Xiaofeng Li1, Qi Yang1, Xian Li1, Xuehong Li1, Wenxing Jin2
Affiliations
  • 1 SPIC Qinghai Photovoltaic Industry Innovation Center Co., Ltd. Xining 810008 China
  • 2 School of Electrical Engineering Southeast University Nanjing 210096 China
Published: 2025-05-20
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With the largescale integration of clean energy sources such as photovoltaics and energy storage into the power grid, grid type control technology has obvious advantages in dealing with voltage stability issues in new energy power systems that lack synchronization. However, how to adaptively control the parameters of grid type photovoltaic storage inverters to maintain voltage stability even when the impedance of the power grid changes is an urgent problem that needs to be solved. Based on this, a method for optimizing the control of optical storage grid inverters using a convolutional neural network optimized by the gazelle algorithm is proposed. Firstly, build a control model for grid type inverters and analyze the stability of output voltage; Secondly, based on the convolutional neural network, an inverter parameter control model is established, and the Gazelle optimization algorithm is utilized to optimize the hyperparameters of the convolutional neural network with strong optimization ability and fast search speed, improving the model's feature learning ability and outputting inverter control parameters; Finally, a certain photovoltaic power generation area was selected for simulation verification. The experiment showed that the proposed grid type photovoltaic inverter control method can adaptively optimize control parameters based on realtime changes in grid impedance, achieve stable voltage output, and have strong practical engineering significance.

clean energy  /  network type control  /  optical storage inverter  /  gazelle algorithm  /  convolutional neural network
Xiulan Pang, Xiaofeng Li, Qi Yang, Xian Li, Xuehong Li, Wenxing Jin. Optimization control technology of optical storage network inverter based on gazelle algorithm optimized convolutional neural network[J]. Renewable Energy Resources, 2025 , 43 (5) : 687 -695 .
Year 2025 volume 43 Issue 5
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  • Receive Date:2024-07-18
  • Online Date:2025-07-16
  • Published:2025-05-20
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  • Received:2024-07-18
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    1 SPIC Qinghai Photovoltaic Industry Innovation Center Co., Ltd. Xining 810008 China
    2 School of Electrical Engineering Southeast University Nanjing 210096 China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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