Condition Parameter Modeling for Anomaly Detection in Wind Turbines
Condition Parameter Modeling for Anomaly Detection in Wind Turbines
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DOI：
10.3390/en7053104
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发表时间：
2014-05
期刊：
影响因子：
3.2
通讯作者：
Yonglong Yan;Jian Li;D. Gao
中科院分区：
文献类型：
--
作者：
Yonglong Yan;Jian Li;D. Gao
从监控和数据采集（SCADA）系统收集的数据在风电场中广泛使用以获得关于风力涡轮机（WT）的操作和状态信息，对于风力涡轮机的异常检测具有重要意义。提出了一种基于SCADA数据的风电涡轮机异常检测模型，并利用BP神经网络自动选择状态参数。通过分析风速的累积概率分布和输出功率与风速的关系，确定SCADA数据集。基于BP神经网络的参数自动选择是为了减少风力涡轮机异常检测的冗余参数。通过对WT故障案例的调查，验证了基于参数自动选择的WT异常检测模型的有效性。
Data collected from the supervisory control and data acquisition (SCADA) system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs), is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN) for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.