An Adaptive Approach for Ice Detection in Wind Turbine With Inductive Transfer Learning

Yun, Hongguang; Zhang, Chengkai; Chao-zhen, Hou; Liu, Zheng

doi:10.1109/access.2019.2926575

Cited by 19 publications

(10 citation statements)

References 25 publications

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“…Ensemble autoencoder 19 Utilized hierarchical features Unlabeled data was not fully utilized CNN 20 High training speed Needs consideration on class-imbalanced data Transfer learning 21 Strengthened generalization ability Features of data were not fully exploited LSTM 22 Utilized temporal relationship between data Needs consideration on class-imbalanced data TL-DNN 27 Dealt with class-imbalanced data Unlabeled data was not fully utilized WT: wind turbine; CNN: convolutional neural network. LSTM: long short-term memory; TL-DNN: triplet loss deep neural network.…”

Section: Presented Model Advantages Challengesmentioning

confidence: 99%

“…Yeh et al 20 combined convolutional neural networks (CNN) and support vector machine to predict long cycle maintenance time of WTs. Yun et al 21 established a well-behaved icing detection model using the SCADA data of one WT and applied the idea of transfer learning to make the model applicable to more WTs. As a specialist for time-series modeling, recurrent neural networks (RNN) have also been explored in WT fault detection.…”

Section: Introductionmentioning

confidence: 99%

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A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

Ding

Wang

Zhang

et al. 2021

International Journal of Distributed Sensor Networks

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Blade icing problems are ubiquitous for wind turbines located in cold climate zones. Data-driven indirect icing detection methods based on supervisory control and data acquisition system have shown strong potential recently. However, the supervisory control and data acquisition data is annotated through manual observation, which will cause the data between normal condition and icing condition to be unlabeled. In addition, the amount of normal data is far more than icing data. The above two issues restrict the performance of most current data-driven models. In order to solve the label missing problem, this article proposes a Pearson correlation coefficient–based algorithm for measuring the degree of blade icing, which calculates the similarity between the unlabeled data and the icing data as its label. Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. Temporal convolutional networks are trained to extract features and make predictions on each subset. The final prediction result is obtained by ensembling the prediction results of all temporal convolutional network models. The proposed model is validated using the actual supervisory control and data acquisition data collected from a wind farm in northern China, and the results indicate that ensuring the consecutiveness and class-balance of the data are quite advantageous for improving the detection accuracy.

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Section: Presented Model Advantages Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

Ding

Wang

Zhang

et al. 2021

International Journal of Distributed Sensor Networks

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“…Recently all three datasets have been used to create general object detection models, which could be used as weight initialization for similar problems and then fine-tuned on the problem dataset allowing quicker training and reducing the need for a labelled dataset. This approach, called transfer learning, allows using a pretrained dataset and model architecture rather than creating an entirely new network based on a limited dataset [18], [19]. In order to ease that process popular deep learning framework called TensorFlow has included a set of pretrained object detection models, typically trained on the datasets mentioned before in the article [20].…”

Section: Related Workmentioning

confidence: 99%

Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection

Pawełczyk

Wojtyra

2020

IEEE Access

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Recent years have shown a noticeable rise in the number of incidents with drones, related to both civilian and military installations. While drone neutralization techniques have become increasingly effective, detection most often relies on professional equipment, which is too expensive to be used for all critical nodes and applications. Therefore, there is a need for drone detection systems that could work on low performance hardware. Its critical component consists of an object detection system. In this article, we introduce a new object detection dataset, built entirely to train computer vision based object detection machine learning algorithms for a task of binary object detection to enable automated, industrial camera based detection of multiple drone objects using camera feed. The dataset expands existing multiclass image classification and object detection datasets (ImageNet, MS-COCO, PASCAL VOC, anti-UAV) with a diversified dataset of drone images. In order to maximize the effectiveness of the model, real world footage was utilized, transformed into images and hand-labelled to create a custom set of 56821 images and 55539 bounding boxes. Additionally, semi-automated labelling was proposed, tested and proved to be very useful for object detection applications. The dataset was divided into train and test subsets for further processing and used to generate 603 easily deployable Haar Cascades as well as 819 high performing Deep Neural Networks based models. They were used to test different object detection methods to determine the long term feasibility of a large scale drone detection system utilizing machine learning algorithms. The study has shown that Haar Cascade can be used as the Minimum Viable Product model for mediocre performance but fails to scale up effectively for a larger dataset compared to the Deep Neural Network model.

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“…In particular, modifying the throttle's electronic system, a load variation in wind turbine can be modeled and seen as ice on the blade. It is well known that the accumulation of ice on the surface of the blades changes its shape and mass, causing an imbalance on the rotor that is rotated mainly by the lift force that comes from the wind [15]. The same effect is produced when an external resistance is added to the electronic circuit of the throttle valve.…”

Section: Introductionmentioning

confidence: 99%

A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems

et al. 2022

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Monitoring the variation of the loading blades is fundamental due to its importance in the behavior of the wind turbine system. Blade performance can be affected by different loads that alter energy conversion efficiency and cause potential safety hazards. An example of this is icing on the blades. Therefore, the main objective of this work is to propose a proportional digital controller capable of detecting load variations in wind turbine blades together with a fault detection method. An experimental platform is then built to experimentally validate the main contribution of the article. This platform employs an automotive throttle device as a blade system emulator of a wind turbine pitch system. In addition, a statistical fault detection algorithm is established based on the point change methodology. Experimental data support our approach.

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An Adaptive Approach for Ice Detection in Wind Turbine With Inductive Transfer Learning

Cited by 19 publications

References 25 publications

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

A PCC-Ensemble-TCN model for wind turbine icing detection using class-imbalanced and label-missing SCADA data

Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection

A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems

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