Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning

2018 ◽  
Author(s):  
Surya Teja Kandukuri ◽  
Huynh Van Khang ◽  
Kjell G. Robbsersmyr
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Feature Learning

scholarly journals Proactive Critical Energy Infrastructure Protection via Deep Feature Learning

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2622
Author(s):  
Konstantina Fotiadou ◽  
Terpsichori Helen Velivassaki ◽  
Artemis Voulkidis ◽  
Dimitrios Skias ◽  
Corrado De Santis ◽  
...  
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Short Term Memory ◽  
Feature Learning ◽  
Slight Modification ◽  
Critical Energy ◽  
Normal Operation ◽  
High Detection Rate ◽  
Energy Infrastructure ◽  
Critical Energy Infrastructure

Autonomous fault detection plays a major role in the Critical Energy Infrastructure (CEI) domain, since sensor faults cause irreparable damage and lead to incorrect results on the condition monitoring of Cyber-Physical (CP) systems. This paper focuses on the challenging application of wind turbine (WT) monitoring. Specifically, we propose the two challenging architectures based on learning deep features, namely—Long Short Term Memory-Stacked Autoencoders (LSTM-SAE), and Convolutional Neural Network (CNN-SAE), for semi-supervised fault detection in wind CPs. The internal learnt features will facilitate the classification task by assigning each upcoming measurement into its corresponding faulty/normal operation status. To illustrate the quality of our schemes, their performance is evaluated against real-world’s wind turbine data. From the experimental section we are able to validate that both LSTM-SAE and CNN-SAE schemes provide high classification scores, indicating the high detection rate of the fault level of the wind turbines. Additionally, slight modification on our architectures are able to be applied on different fault/anomaly detection categories on variant Cyber-Physical systems.

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