A Fuzzy Inference-Based Fault Detection Scheme Using Adaptive Thresholds for Health Monitoring of Offshore Wind-Farms
A Fuzzy Inference-Based Fault Detection Scheme Using Adaptive Thresholds for Health Monitoring of Offshore Wind-Farms
复制标题
DOI：
10.1109/jsen.2014.2347700
复制
发表时间：
2014-11-01
影响因子：
4.3
通讯作者：
Kishor, Nand
中科院分区：
文献类型：
--
作者：
Agarwal, Deepshikha;Kishor, Nand
所提出的灵活阈值选择和基于模糊推理系统的故障检测系统（FTSFFDS）是一种简单有效的故障定位方法，为海上风电场结构健康监测奠定了基础。根据塔上安装的传感器获得的实际参数读数，可以远程观察和监控风电场的健康状况。该模拟考虑了白天和夜间收集的波高和海面温度数据。数据集之间的相关性表明自适应（动态）阈值的选择。该方法涉及量化以从收集的样本中提取有意义的信息。提出了一种模糊推理系统，该系统利用接收数据的组合求和和流向来准确预测风电场的故障。该方法与原始均值法进行了比较。所提出的方法提供了一种检测实时故障发生的简单方法，并且实际上有助于显着减小消息大小，从而将无线传感器网络的网络寿命延长近十倍。结果证实，所提出的方法 FTSFFDS 比现有方法具有更好的故障预测精度。
The proposed flexible threshold selection and fuzzy inference system-based fault detection system (FTSFFDS) is a simple and efficient method of fault localization which forms the basis for the structural health monitoring of offshore wind farms. Based on the actual parameter readings obtained by the sensors attached to the towers, one can remotely observe and monitor the health condition of wind farm. The simulation considers day time and night time collected data for wave-height and sea-surface temperatures. The correlation between the data sets indicates the choice of adaptive (dynamic) thresholds. The method involves quantization for extracting meaningful information from the collected samples. A fuzzy inferencing system is proposed which uses combination-summation and flow-direction of received data to accurately predict faults in the wind farm. The method is compared with primitive mean method. The proposed method provides a simple approach for detecting real-time fault occurrences and in-effect helps in reducing the message size considerably to increase the network lifetime of the wireless sensor network by nearly ten times. The results confirm that the proposed method FTSFFDS has better fault-prediction accuracy over the existing method.