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A new energy power grid security and stability control method based on time series convolutional residual network and pelican optimization algorithm
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Jianxin Zhang1, Jian Qiu1, Yukun Zhu2, 3, Yihua Zhu2, 3, Huanhuan Yang1, Guanghu Xu, Liang Tu2, 3
Renewable Energy Resources | 2024, 42(6) : 845 - 852
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Renewable Energy Resources | 2024, 42(6): 845-852
A new energy power grid security and stability control method based on time series convolutional residual network and pelican optimization algorithm
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Jianxin Zhang1, Jian Qiu1, Yukun Zhu2, 3, Yihua Zhu2, 3, Huanhuan Yang1, Guanghu Xu, Liang Tu2, 3
Affiliations
  • 1 China Southern Power Grid Co., Ltd. Guangzhou 510663 China
  • 2 National Key Laboratory of DC Transmission Technology (China Southern Power Grid Research Institute Co., Ltd.) Guangzhou 510663 China
  • 3 Guangdong Provincial Key Laboratory of Intelligent Operation and Control of New Energy Power System Guangzhou 510663 China
Published: 2024-06-20
Outline
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With the advancement of the "dual carbon" goal, the scale and capacity of randomly fluctuating new energy connected to the power grid are increasingly increasing, seriously affecting the safe and stable operation of the power grid. This paper proposes a power grid voltage security and stability control strategy based on time series convolutional residual network and Pelican optimization algorithm for the problem of voltage stability control in large disturbance faults. Firstly, taking advantage of the advantages of low loss of temporal convolutional information, wide receptive field, and strong deep feature extraction ability of residual networks, a voltage stability prediction model based on temporal convolutional residual networks is constructed, mapping the relationship between sensitive node voltage temporal features and voltage stability; Secondly, a voltage stability control model is constructed to output control strategies, and the Pelican optimization algorithm is utilized to solve the control model with its fast convergence speed and strong search ability, resulting in the optimal measures for machine and load shedding actions. Finally, after simulation and verification, the experimental results show that the proposed method improves the accuracy of voltage safety and stability prediction in the power grid, and improves the safe and stable operation level of the power grid after faults through the optimal voltage stability control strategy.

new energy  /  large interference fault  /  time series convolutional residual network  /  pelican optimization algorithm  /  security and stability control
Jianxin Zhang, Jian Qiu, Yukun Zhu, Yihua Zhu, Huanhuan Yang, Guanghu Xu, Liang Tu. A new energy power grid security and stability control method based on time series convolutional residual network and pelican optimization algorithm[J]. Renewable Energy Resources, 2024 , 42 (6) : 845 -852 .
Year 2024 volume 42 Issue 6
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Article Info
  • Receive Date:2023-12-13
  • Online Date:2025-07-22
  • Published:2024-06-20
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  • Received:2023-12-13
Funding
Affiliations
    1 China Southern Power Grid Co., Ltd. Guangzhou 510663 China
    2 National Key Laboratory of DC Transmission Technology (China Southern Power Grid Research Institute Co., Ltd.) Guangzhou 510663 China
    3 Guangdong Provincial Key Laboratory of Intelligent Operation and Control of New Energy Power System Guangzhou 510663 China
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表12种不同金属材料的力学参数

Family
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Number of
genus
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Number of
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占总种数比例
Percentage 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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