Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

Abstract

The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is, they are labeled, normalized, and balanced. The features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset. A middle feature is proposed to show how a given feature is correlated with icing on the blade. Performance indicators for the model, including a reward function, are also designed to assess its predictive accuracy. Finally, the most suitable model is used to predict the testing data, and values of the reward function and the predictive accuracy of the model are calculated. The proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle feature. The results here show that an integrated indicator system is superior to a single indicator of accuracy when evaluating the prediction model.

A Review of Wind Turbine Icing and Anti/De-Icing Technologies

“…Recently, some artificial intelligence methods have also been introduced to measure wind turbine icing [82,83,[89][90][91][92][93][94][95]. Yi et al [96] used a large number of data and relevant information of time series, combined the original data, features extracted by a Stacked Auto Encoder (SAE) and a residual vector to obtain discriminant features, tested the operating state of wind turbines, and proposed a fault detection scheme based on discriminant feature learning.…”

Introduction to the Special Issue on Hybrid Intelligent Methods for Forecasting in Resources and Energy Field

“…The thirteenth paper "Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network" by Li et al [13] proposes a method to build a universal model based on a deep neural network (DNN) by using the data of supervisory control and data acquisition system (SCADA). This paper provides a universal icing detection model based on DNN.…”

A Review of Wind Turbine Icing Prediction Technology

“…The technique may relate continuously transmitted features to the binary state of turbine blade icing by using intermediate feature variables. Experiment findings suggest that the integrated metric system outperforms a single accuracy measure when assessing prediction models [54]. Cui et al performed icing impeller model tests as well as natural world icing trials before recommending a deep neural network-based approach for predicting icing quality in 2022 [55].…”

“…In 2022, Li et al published a generalized DNN-based model for predicting wind turbine icing situations that is based on data from SCADA systems and has a high combined accuracy [64]. Previously, in 2021, Chen et al developed TrAdaBoost, a ground-breaking transfer learning algorithm that has been shown to increase performance in dealing with imbalances and varying distributions of wind turbine data [65].…”