Output Power Modeling of Wind Turbine Based on State Curve Analysis

2021 ◽  
Vol 02 ◽  
Author(s):  
Jiaying Huang ◽  
Wangqiang Niu ◽  
Xiaotong Wang
Keyword(s):  
Power Generation ◽  
Feature Selection ◽  
Wind Turbine ◽  
Polynomial Regression ◽  
Modeling Method ◽  
Curve Analysis ◽  
Mechanical Transmission ◽  
Power Curve ◽  
Power Modeling ◽  
State Curve

Background: In wind power generation, the power curve can reflect the overall power generation performance of a wind turbine. How to make the power curve have high precision and be easy to interpret is a hot research topic. Objective: Because the current power curve modeling method is not comprehensive in feature selection, the simplified model and state curve of a wind turbine are introduced to avoid feature selection and make the model interpret easily. Methods: A power modeling method based on different working conditions is proposed. The wind turbine system is simplified into three physical models of blades, mechanical transmission and generator, and the energy transfer is expressed by mathematical expressions. The operation process of the wind turbine is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT), and the power expression of each phase is given after the analysis of state curves. Results: The effectiveness of the proposed method is verified by the supervisory control and data acquisition (SCADA) data of a 2MW wind turbine. The experimental results show that the mean absolute percentage error (MAPE) index of the proposed power modeling method based on state curve analysis is 11.56%, which indicates that the power prediction result of this method is better than that of the sixth-order polynomial regression method, whose MAPE is 13.88%. Conclusion: The results show that the proposed method is feasible with high transparency and is interpreted easily.

scholarly journals Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1105 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Standard Deviation ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Curve Analysis ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Features Selection ◽  
Power Curve ◽  
Working Parameters ◽  
Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

Statistical power curve modeling to estimate wind turbine power output

2019 ◽  
pp. 0309524X1989167
Author(s):  
Bharti Dongre ◽  
Rajesh Kumar Pateriya
Keyword(s):  
Wind Turbine ◽  
Statistical Power ◽  
Polynomial Regression ◽  
Smoothing Splines ◽  
Optimal Number ◽  
Specific Power ◽  
Power Curve ◽  
Curve Modeling ◽  
A Site ◽  
Power Curves

In the wind industry, the power curve serves as a performance index of the wind turbine. The machine-specific power curves are not sufficient to measure the performance of wind turbines in different environmental and geographical conditions. The aim is to develop a site-specific power curve of the wind turbine to estimate its output power. In this article, statistical methods based on empirical power curves are implemented using various techniques such as polynomial regression, splines regression, and smoothing splines regression. In the case of splines regression, instead of randomly selecting knots, the optimal number of knots and their positions are identified using three approaches: particle swarm optimization, half-split, and clustering. The National Renewable Energy Laboratory datasets have been used to develop the models. Imperial investigations show that knot-selection strategies improve the performance of splines regression. However, the smoothing splines-based power curve model estimates more accurately compared with all others.

scholarly journals Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect

Energies ◽  
2017 ◽  
Vol 10 (3) ◽  
pp. 395 ◽  
Author(s):  
Jie Tian ◽  
Dao Zhou ◽  
Chi Su ◽  
Mohsen Soltani ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Wind Farms ◽  
Power Curve ◽  
Wake Effect ◽  
Curve Design ◽  
Optimal Power

Detection of icing on wind turbine blades by means of vibration and power curve analysis

Wind Energy ◽  
2015 ◽  
Vol 19 (10) ◽  
pp. 1819-1832 ◽  
Author(s):  
Georgios Alexandros Skrimpas ◽  
Karolina Kleani ◽  
Nenad Mijatovic ◽  
Christian Walsted Sweeney ◽  
Bogi Bech Jensen ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Curve Analysis ◽  
Power Curve ◽  
Wind Turbine Blades

Analysis of parametric and non-parametric regression techniques to model the wind turbine power curve

2018 ◽  
Vol 43 (3) ◽  
pp. 225-232 ◽  
Author(s):  
Rajesh Wadhvani ◽  
Sanyam Shukla
Keyword(s):  
Power System ◽  
Wind Turbine ◽  
Wind Power ◽  
Polynomial Regression ◽  
Technical Specification ◽  
Smoothing Spline ◽  
Power Curve ◽  
Spline Regression ◽  
Regression Techniques ◽  
Weighted Polynomial Regression

Wind turbine power curve provides technical specification of the wind turbine in the form of nominal wind power readings. This information may used to monitor the performance of the power system, estimate the power produced by the turbine, optimize the operational cost, and improve the reliability of the power system. However, this information is not sufficient to accomplish these tasks. To accomplish these tasks, the accurate modeling of the wind power curve is required. In this article, various curve fitting techniques, namely polynomial regression, locally weighted polynomial regression, spline regression, piecewise polynomial regression, and smoothing spline, have been applied to model the power curve of wind turbine. All these techniques have been used to model the power curve on National Renewable Energy Laboratory (NREL) 2012 dataset with site-id 124693.

Quantile based probabilistic wind turbine power curve model

2021 ◽  
Vol 296 ◽  
pp. 116913
Author(s):  
Keyi Xu ◽  
Jie Yan ◽  
Hao Zhang ◽  
Haoran Zhang ◽  
Shuang Han ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Power Curve ◽  
Curve Model
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