2018
DOI: 10.1049/iet-rpg.2017.0867
|View full text |Cite
|
Sign up to set email alerts
|

DNN‐based approach for fault detection in a direct drive wind turbine

Abstract: Incipient fault detection of wind turbines is beneficial for making maintenance strategy and avoiding catastrophic result in a wind farm. A deep neural network (DNN)-based approach is proposed to deal with the challenging task for a direct drive wind turbine, involving four steps: a preprocessing method considering operational mechanism is presented to get rid of the outliers in supervisory control and data acquisition (SCADA); the conventional random forest method is used to evaluate the importance of variabl… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
31
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
6
2

Relationship

0
8

Authors

Journals

citations
Cited by 54 publications
(31 citation statements)
references
References 26 publications
0
31
0
Order By: Relevance
“…WSNs can be used in almost every domain and field now. In [18], the scenario was based on wind turbines and incipient fault detection in wind turbines. It was carried out through Deep Neural Network (DNN).…”
Section: Related Workmentioning
confidence: 99%
“…WSNs can be used in almost every domain and field now. In [18], the scenario was based on wind turbines and incipient fault detection in wind turbines. It was carried out through Deep Neural Network (DNN).…”
Section: Related Workmentioning
confidence: 99%
“…In order to establish a model for bearing failure detection, we are supposed to determine the input variables in X and the output variable y properly. In [7,8], I, U (1) , T (4) , T (5) , T (6) and P, V, I, T (3) are used as input variables to predict bearing temperature. Inspired by their work, we choose T (1) as output variable y, the rest of the ten features in Table 1 as input variables with X = (P, V, I, U (1) , U (2) , T (2) , T (3) , T (4) , T (5) , T (6)…”
Section: Model Construction and Evaluationmentioning
confidence: 99%
“…To evaluate the performance of the selected features, we adopt two metrics, namely mean decrease in accuracy and mean decrease in node impurity. Their definition and calculation can be found in [4,20]. Fig.…”
Section: Model Construction and Evaluationmentioning
confidence: 99%
See 1 more Smart Citation
“…Such discrepancies will reflect whether the machine is in normal or failure mode, which requires a classifier to judge. Recently, some Artificial Intelligent (AI) classifiers, such as neural networks [7][8][9][10][11][12], machine learning methods [13][14][15], and deep learning methods [16,17], have been widely applied in classifying the incipient faults of wind turbines. These methods are really very effective for some faults within a certain working state, but it seems impossible for them to diagnose other faults under other working states.…”
Section: Introductionmentioning
confidence: 99%