论文标题
通过动态参考状态估计在风力涡轮机中的性能故障检测
Performance Fault Detection in Wind Turbines by Dynamic Reference State Estimation
论文作者
论文摘要
风公园的运营和维护成本占公园整体终生成本的主要部分。它们还包括避免发电不足的损失收入的机会成本。我们提出了一种基于机器学习的决策支持方法，该方法将这些机会成本降至最低。通过分析来自涡轮机操作的遥测传感器数据流，估计高度准确的功率参考关系和基准测试，我们可以以涡轮和特定地点的方式检测与性能相关的操作故障。根据机器学习算法和回归器集的组合选择最准确的功率参考模型。如果超过正常的操作状态边界，则可以提醒个人操作个人。我们在案例研究中证明了用于商业网格连接的陆上风力涡轮机的性能故障检测方法。诊断出检测到的表现不佳事件，我们发现观察到的发电缺陷与与涡轮刀片刀片执行器低液压相关的转子叶片不一致一致。
The operation and maintenance costs of wind parks make up a major fraction of a park's overall lifetime costs. They also include opportunity costs of lost revenue from avoidable power generation underperformance. We present a machine-learning based decision support method that minimizes these opportunity costs. By analyzing the stream of telemetry sensor data from the turbine operation, estimating highly accurate power reference relations and benchmarking, we can detect performance-related operational faults in a turbine- and site-specific manner. The most accurate power reference model is selected based on combinations of machine learning algorithms and regressor sets. Operating personal can be alerted if a normal operating state boundary is exceeded. We demonstrate the performance fault detection method in a case study for a commercial grid-connected onshore wind turbine. Diagnosing a detected underperformance event, we find that the observed power generation deficiencies coincide with rotor blade misalignment related to low hydraulic pressure of the turbine's blade actuators.
