风力轮机状况监测使用SSA优化自主注意BiLSTM网络和变化点检测算法
Junshuai Yan1, Yongqian Liu1, Li Li1
1School of New Energy, North China Electric Power University, Beijing 102206, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
风力轮机的新状态监测方法 (SSD) 使用子搜索 (SSA) 和自我注意BiLSTM (SABiLSTM) 早期检测故障. 这种方法显著提高了预测准确性,并使提前检测故障,降低运营和维护成本.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 预测性维护是指预测性维护.
背景情况:
- 风力轮机的运营和维护 (O&M) 成本很大,影响了整体可靠性和经济可行性.
- 有效的状态监测和异常检测对于最大限度地降低O&M费用和提高风力轮机性能至关重要.
- 现有的方法经常与风力轮SCADA数据中存在的复杂,非线性动力学和时空特征作斗争.
研究的目的:
- 为风力轮机提出一种新的状态监测方法 (SSA-SABiLSTM-BinSegCPD,SSD).
- 加强异常检测和早期识别风力轮机条件恶化的情况.
- 提高风力轮机健康监测系统的预测准确度和可靠性.
主要方法:
- 使用双层双向长期短期记忆 (BiLSTM) 网络与自我注意机制 (SABiLSTM) 集成的双层双向长期短期记忆 (BiLSTM) 网络开发一个正常行为模型,以捕获SCADA数据中的非线性动态和时空特征.
- 使用子搜索算法 (SSA) 优化SABiLSTM模型,以提高搜索精度和收率.
- 将二进制细分变化点检测算法 (BinSegCPD) 应用于预测的残余序列,以自动识别风力轮机损坏.
主要成果:
- 与基线模型相比,拟议的SSA-SABiLSTM模型显示出更高的预测准确性,平均绝对误差 (MAE),根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 显著减少.
- 具体的改善包括MAE下降47.23%,RMSE下降42.19%,MAPE下降53.38%,R2与SABiLSTM模型相比增加4.6%.
- 完整的SSD方法成功地提前47-120小时检测到恶化条件,并在实际故障前大约36小时触发故障报警.
结论:
- SSA-SABiLSTM-BinSegCPD (SSD) 方法为风力轮机的状态监测和异常检测提供了强大而准确的方法.
- 整合SSA优化和自我注意机制显著提高了BiLSTM网络风力轮机健康评估的预测能力.
- 固态硬件 (SSD) 方法通过早期检测故障,从而降低O&M成本并提高风力轮机的可靠性,提供了实际的好处.
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