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Considering the health status of wind turbines and the dual attention mechanism CNN-BiLSTM ultra-short-term power prediction
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Kaiwei Zhang1, Zhong Wen1, Shengpeng Yang1, Zihan Hu1, Jian Ding2
Renewable Energy Resources | 2025, 43(2) : 217 - 224
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Renewable Energy Resources | 2025, 43(2): 217-224
Considering the health status of wind turbines and the dual attention mechanism CNN-BiLSTM ultra-short-term power prediction
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Kaiwei Zhang1, Zhong Wen1, Shengpeng Yang1, Zihan Hu1, Jian Ding2
Affiliations
  • 1 School of Electrical and New Energy Three Gorges University Yichang 443002 China
  • 2 Shanghai Survey and Design Institute Co., Ltd. Shanghai 200434 China
Published: 2025-02-20
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In order to improve the accuracy of ultrashortterm power prediction of wind turbines, this paper proposes a CNNBiLSTM ultrashortterm power prediction method considering the health status of wind turbines and dual attention mechanism. Firstly, considering the influence of the interaction between the environmental factors and the components of the wind turbine on the output power of the wind turbine, he relative error of the normal operation of each component of the wind turbine is used as the deterioration degree of the monitoring index. Secondly, the fuzzy comprehensive evaluation method assesses the health of wind turbines, and the historical data set is categorized based on the evaluation results. Finally, the dual attention mechanism CNN BiLSTM model is used to construct an ultrashortterm power prediction model for the classified data set. The experimental results show that the RMSE and MAE considering the health status of wind turbines are reduced by 17.3% and 20.5% respectively compared with the RSME and MSE without considering the health status of wind turbines.

ultra-short-term  /  power prediction  /  health status  /  dual attention mechanism  /  CNN-BILSTM model
Kaiwei Zhang, Zhong Wen, Shengpeng Yang, Zihan Hu, Jian Ding. Considering the health status of wind turbines and the dual attention mechanism CNN-BiLSTM ultra-short-term power prediction[J]. Renewable Energy Resources, 2025 , 43 (2) : 217 -224 .
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Year 2025 volume 43 Issue 2
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Article Info
  • Receive Date：2024-05-14
  • Online Date：2025-07-18
  • Published：2025-02-20
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  • Received：2024-05-14
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Affiliations
    1 School of Electrical and New Energy Three Gorges University Yichang 443002 China
    2 Shanghai Survey and Design Institute Co., Ltd. Shanghai 200434 China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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