کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6764087 1431577 2018 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
پیش نمایش صفحه اول مقاله
An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring
چکیده انگلیسی
The vast installment of wind turbines and the development of condition monitoring system provides large amounts of operational data for condition monitoring and health management, while the lack of labeled data becomes one of the major challenges for the data analytics. To address this issue, this work presents an unsupervised anomaly detection approach for wind turbine condition monitoring, where a spatiotemporal graphical modeling method, spatiotemporal pattern network (STPN), is applied to extract the spatial and temporal features between the variables in the system, and an energy-based model, stacked Restricted Boltzmann Machine (RBM) is used to capture the system-wide patterns and then applied for condition monitoring. Case studies on three data sets are carried out including: (1) anomaly detection on a benchmark model for fault detection and isolation, (2) anomaly detection on an experimental data set with the normal condition and 11 fault conditions and (3) online condition monitoring using real data from a wind farm in northwest China. The results show that the proposed approach is capable of detecting the anomalies without the need for labeling data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable Energy - Volume 127, November 2018, Pages 230-241
نویسندگان
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