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Health Status Assessment for Wind Turbine with Recurrent Neural Networks
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-12-05
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
In order to improve the safety, efficiency, and reliability in large scale wind turbines, a great deal of statistical and machine-learning models for wind turbine health monitoring system (WTHMS) are proposed based on SCADA variables.
The data-driven WTHMS have been performed widely with the attentions on predicting the failures of the wind turbine or primary components.
However, the health status of wind turbine often degrades gradually rather than suddenly.
Thus, the SCADA variables change continuously to the occurrence of certain faults.
Inspired by the ability of recurrent neural network (RNN) in redefining the raw sensory data, we introduce a hybrid methodology that combines the analysis of variance for each sequential SCADA variable with RNN to assess the health status of wind turbine.
First, each original sequence is split by different variance ranges into several categories to improve the generalized ability of the RNN.
Then, the long short-term memory (LSTM) is procured on the normal running sequence to learn the gradually changing situations.
Finally, a weighted assessment method incorporating the health of primary components is applied to judge the health level of the wind turbine.
Experiments on real-world datasets from two wind turbines demonstrate the effectiveness and generalization of the proposed model.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sun, Zexian& Sun, Hexu. 2018. Health Status Assessment for Wind Turbine with Recurrent Neural Networks. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1208659
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sun, Zexian& Sun, Hexu. Health Status Assessment for Wind Turbine with Recurrent Neural Networks. Mathematical Problems in Engineering No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1208659
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sun, Zexian& Sun, Hexu. Health Status Assessment for Wind Turbine with Recurrent Neural Networks. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1208659
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208659
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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