SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

Castellani, Francesco; Astolfi, Davide; Natili, Francesco

doi:10.3390/app11083307

Cited by 31 publications

(27 citation statements)

References 37 publications

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“…Because the operation of the pitch system directly affects the power output of the wind turbine, the most important parameter of the pitch control system is power output. Based on the principal component analysis of wind turbine data, [37] the Pearson correlation coefficient will be used to analyze the correlation between power output, which used as the main variable, and other parameters in the process of feature selection, and the parameters with a high correlation with the pitch control system will be retained, to perform the second cleaning and processing of the data.…”

Section: Ihho-lightgbm Fault Detection Model 41 Data Cleaning and Preprocessingmentioning

confidence: 99%

Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM

Tang

Peng

2021

Applied Sciences

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To address the issue of a large calculation and difficult optimization for the traditional fault detection of a wind turbine-based pitch control system, a fault detection model, based on LightGBM by the improved Harris Hawks optimization algorithm (light gradient boosting machine by the improved Harris Hawks optimization,IHHO-LightGBM) for the wind turbine-based pitch control system, is proposed in this article. Firstly, a trigonometric function model is introduced by IHHO to update the prey escape energy, to balance the global exploration ability and local development ability of the algorithm. In this model, the fault detection false alarm rate is used as the fitness function, and the two parameters are used as the optimization objects of the improved Harris Hawks optimization algorithm, to optimize the parameters, so as to achieve the global optimal parameters to improve the performance of the fault detection model. Three different fault data of the pitch control system in actual operations of domestic wind farms are used as the experimental data, the Pearson correlation analysis method is introduced, and the wind turbine power output is taken as the main state parameter, to analyze the correlation degree of all the characteristic variables of the data and screen the important characteristic variables out, so as to achieve the effective dimensionality reduction process of the data, by using the feature selection method. Three established fault detection models are selected and compared with the proposed method, to verify its feasibility. The experimental data indicate that compared with other algorithms, the fault detecting ability of the proposed model is improved in all aspects, and the false alarm rate and false negative rate are lower.

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Section: Ihho-lightgbm Fault Detection Model 41 Data Cleaning and Preprocessingmentioning

confidence: 99%

Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM

Tang

Peng

2021

Applied Sciences

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“…Besides, in [ 18 ], the authors also proposed a data preprocessing procedure including data cleaning, feature selection, feature reduction, and data set balancing. In [ 19 ], the authors constructed a normal behavior model using support vector regression with a Gaussian kernel to diagnose the faults of wind turbine generators, and the dimensionality of features was reduced by using principal component analysis. Kong et al [ 20 ] introduced a feature selection method with Pearson correlation coefficients in their fault detection model to diagnose the gearbox failures of a wind turbine.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 21 , 22 ], a method was used that included decision trees to achieve the purpose of condition monitoring. Feature selection was used for variables that can reflect a special component condition in [ 18 , 19 , 20 ]. However, in this study, the obtained SCADA datasets have only one fault of blade breakages, and no SCADA variables can directly indicate the conditions of blades.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Wang

Zhang

et al. 2021

Sensors

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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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“…The model hyperparameters have been optimized using a procedure similar to [37], which is a useful reference as regards the use of SVR regressions in wind energy applications. The model is called 30 times, with different hyperparameter set ups, and 5-fold cross validation is performed [38].…”

mentioning

confidence: 99%

“…The model is called 30 times, with different hyperparameter set ups, and 5-fold cross validation is performed [38]. At each model call, the SVR parameters are changed randomly and these are the [37] box constraint, the kernel scale and . The model set up resulting in the best objective function (which is the log of 1 plus cross-validation loss) is selected.…”

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confidence: 99%

Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Astolfi

2021

Machines

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Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated.

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SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

Cited by 31 publications

References 37 publications

Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM

Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM

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

Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

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