The discriminant model of wind turbine SCADA normal data based on random forest

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter α to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods.

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An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring

Sensors ◽

10.3390/s21165654 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5654

Author(s):

Guo Li ◽

Chensheng Wang ◽

Di Zhang ◽

Guang Yang

Keyword(s):

Feature Selection ◽

Random Forest ◽

Wind Turbine ◽

Condition Monitoring ◽

Supervisory Control ◽

Feature Selection Method ◽

Selection Method ◽

Random Forest Algorithm ◽

Gated Recurrent Unit ◽

Euclidian Distances

Feature selection and dimensionality reduction are important for the performance of wind turbine condition monitoring models using supervisory control and data acquisition (SCADA) data. In this paper, an improved random forest algorithm, namely Feature Simplification Random Forest (FS_RF), is proposed, which is capable of identifying features closely correlated with wind turbine working conditions. The Euclidian distances are employed to distinguish the weight of the same feature among different samples, and its importance is measured by means of the random forest algorithm. The selected features are finally verified by a two-layer gated recurrent unit (GRU) neural network facilitating condition monitoring. The experimental results demonstrate the capacity and effectiveness of the proposed method for wind turbine condition monitoring.

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Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF

Sensors ◽

10.3390/s21186215 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6215

Author(s):

Mingzhu Tang ◽

Jiabiao Yi ◽

Huawei Wu ◽

Zimin Wang

Keyword(s):

Random Forest ◽

Fault Detection ◽

Wind Turbine ◽

Fault Model ◽

Training Set ◽

Turbine Generator ◽

Data Set ◽

Test Set ◽

Wind Turbine Generator ◽

Pitch System

It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

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A new integrated analytics approach for wind turbine fault detection using wavelet, RLS filter and random forest

Energy Sources Part A Recovery Utilization and Environmental Effects ◽

10.1080/15567036.2019.1677815 ◽

2019 ◽

pp. 1-16

Author(s):

Shiyao Qin ◽

Mengzhou Zhang ◽

Xiaojing Ma ◽

Mei Li

Keyword(s):

Random Forest ◽

Fault Detection ◽

Wind Turbine

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Diagnosis of Blade Icing Using Multiple Intelligent Algorithms

Energies ◽

10.3390/en13112975 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2975

Author(s):

Xiyun Yang ◽

Tianze Ye ◽

Qile Wang ◽

Zhun Tao

Keyword(s):

Neural Network ◽

Random Forest ◽

Wind Turbine ◽

Wind Farm ◽

Turbine Blades ◽

Back Propagation ◽

Active Power ◽

K Nearest Neighbor ◽

Wind Turbine Blades ◽

Fully Connected

The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods.

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Mode classification for vortex shedding from an oscillating wind turbine using machine learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/2141/1/012009 ◽

2021 ◽

Vol 2141 (1) ◽

pp. 012009

Author(s):

Matthew Cann ◽

Ryley McConkey ◽

Fue-Sang Lien ◽

William Melek ◽

Eugene Yee

Keyword(s):

Machine Learning ◽

Random Forest ◽

Wind Turbine ◽

Vortex Shedding ◽

Classification Accuracy ◽

Supervised Machine Learning ◽

Series Data ◽

Oscillating Cylinder ◽

Local Flow ◽

Testing Accuracy

Abstract This study presents an effective strategy that applies machine learning methods to classify vortex shedding modes produced by the oscillating cylinder of a bladeless wind turbine. A 2-dimensional computational fluid dynamic (CFD) simulation using OpenFOAMv2006 was developed to simulate a bladeless wind turbines vortex shedding behavior. The simulations were conducted at two wake modes (2S, 2P) and a transition mode (2PO). The local flow measurements were recorded using four sensors: vorticity, flow speed, stream-wise and transverse stream-wise velocity components. The time-series data was transformed into the frequency domain to generate a reduced feature vector. A variety of supervised machine learning models were quantitatively compared based on classification accuracy. The best performing models were then reevaluated based on the effects of artificial noisy experimental data on the models’ performance. The velocity sensors orientated transverse to the pre-dominant flow (u y ) achieved improved testing accuracy of 15% compared to the next best sensor. The random forest and k-nearest neighbor models, using u y , achieved 99.3% and 99.8% classification accuracy, respectively. The feature noise analysis conducted reduced classification accuracy by 11.7% and 21.2% at the highest noise level for the respective models. The random forest algorithm trained using the transverse stream-wise component of the velocity vector provided the best balance of testing accuracy and robustness to data corruption. The results highlight the proposed methods’ ability to accurately identify vortex structures in the wake of an oscillating cylinder using feature extraction.

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