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一个集成网络,用于高精度和长期预测风力轮机上的结冰情况
Jiazhi Dai1,2, Mario Rotea2,3, Nasser Kehtarnavaz1,2
1Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
准确的风力轮机结冰预测对于防止电力损失至关重要. 一个新的深度学习模型PCTG (平行CNN-TCN GRU) 预测了刀片结冰的时间,提前22天,准确率超过97%.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 人工智能的人工智能
- 气象学 天气学
背景情况:
- 风力轮机结冰显著降低了发电效率.
- 预测性维护对于优化风电场运营至关重要.
- SCADA数据为预测环境影响提供了宝贵的见解.
研究的目的:
- 为风力轮机叶片开发一个高精度的长期结冰预测模型.
- 为了利用SCADA传感器数据进行先进的风能预测.
- 为了减轻风力轮机结冰造成的功率损失.
主要方法:
- 一个新的深度学习网络PCTG (平行CNN-TCN GRU) 被开发出来.
- 该模型整合了卷积神经网络 (CNN),时间卷积网络 (TCN) 和门式循环单元 (GRU).
- 来自多台风力轮机的SCADA时间序列数据被用于培训和验证.
主要成果:
- 该PCTG模型在最多2天的时间范围内实现了平均预测准确率约97%的预测准确度.
- 保持了超过95%的准确性,长期预测可以提前22天.
- 在一个单独的数据集上,对10天前的结冰预测的准确性超过99%.
结论:
- PCTG模型为准确和长期的风力轮机结冰预测提供了一个强大的解决方案.
- 这种深度学习方法有效地捕捉了SCADA数据中的时间依赖性和变量相互作用.
- 这些发现支持采取主动措施,防止电力损失,提高风能可靠性.
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