Title :
A method based on artificial neural network to estimate the health of wind turbine
Author :
Hui Li ; Jiarong Yang ; Menghang Zhang ; Shuangquan Guo ; Wei Lv ; Zongchang Liu
Author_Institution :
Central Academe, Shanghai Electr. Group Co., Ltd., Shanghai, China
Abstract :
This paper proposes a method based on the artificial neural network model to evaluate health state of wind turbine by using SCADA data. In this study, the core idea is to analysis the health condition of wind turbine by BP-ANN model, a kind of supervised learning technique is used in this proposed model, by selecting standard data as baseline data and compare with current testing data can realize the evaluation the state of wind turbines. By verifying the validation of the model through real SCADA data, and by visualization method and BP neural network to realize health assessment. This article focuses on the health status of the wind turbines, and provide a method to assess performance. Experimental results show that this method as an effective tool that can achieve the health assessment of wind turbines.
Keywords :
SCADA systems; backpropagation; condition monitoring; data visualisation; neural nets; power engineering computing; wind turbines; BP neural network; BP-ANN model; SCADA data; artificial neural network model; health assessment; health condition; performance assessment; supervised learning technique; visualization method; wind turbine health estimation; wind turbine health state evaluation; wind turbine health status; Artificial neural networks; Data models; Estimation; Prognostics and health management; Training; Wind turbines; ANN; Health Estimation; Wind Turbine;
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
DOI :
10.1109/CCDC.2015.7162050