Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

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Statistical power curve modeling to estimate wind turbine power output

Wind Engineering ◽

10.1177/0309524x19891671 ◽

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.

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Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression

Applied Sciences ◽

10.3390/app11073048 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3048

Author(s):

Bo Jing ◽

Zheng Qian ◽

Hamidreza Zareipour ◽

Yan Pei ◽

Anqi Wang

Keyword(s):

Quantile Regression ◽

Wind Turbine ◽

Wind Power ◽

Power Distribution ◽

Wind Farms ◽

Power Curve ◽

Energy Assessment ◽

Curve Modeling ◽

Relationship Of ◽

Logistic Functions

The wind turbine power curve (WTPC) is of great significance for wind power forecasting, condition monitoring, and energy assessment. This paper proposes a novel WTPC modelling method with logistic functions based on quantile regression (QRLF). Firstly, we combine the asymmetric absolute value function from the quantile regression (QR) cost function with logistic functions (LF), so that the proposed method can describe the uncertainty of wind power by the fitting curves of different quantiles without considering the prior distribution of wind power. Among them, three optimization algorithms are selected to make comparative studies. Secondly, an adaptive outlier filtering method is developed based on QRLF, which can eliminate the outliers by the symmetrical relationship of power distribution. Lastly, supervisory control and data acquisition (SCADA) data collected from wind turbines in three wind farms are used to evaluate the performance of the proposed method. Five evaluation metrics are applied for the comparative analysis. Compared with typical WTPC models, QRLF has better fitting performance in both deterministic and probabilistic power curve modeling.

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Assessment and Performance Evaluation of a Wind Turbine Power Output

Energies ◽

10.3390/en11081992 ◽

2018 ◽

Vol 11 (8) ◽

pp. 1992 ◽

Cited By ~ 6

Author(s):

Akintayo Abolude ◽

Wen Zhou

Keyword(s):

Time Series ◽

Wind Turbine ◽

Wind Power ◽

Power Output ◽

Estimation Error ◽

Power Curve ◽

Estimation Errors ◽

Site Specific ◽

Wind Speeds ◽

And Performance

Estimation errors have constantly been a technology bother for wind power management, often time with deviations of actual power curve (APC) from the turbine power curve (TPC). Power output dispersion for an operational 800 kW turbine was analyzed using three averaging tine steps of 1-min, 5-min, and 15-min. The error between the APC and TPC in kWh was about 25% on average, irrespective of the time of the day, although higher magnitudes of error were observed during low wind speeds and poor wind conditions. The 15-min averaged time series proved more suitable for grid management and energy load scheduling, but the error margin was still a major concern. An effective power curve (EPC) based on the polynomial parametric wind turbine power curve modeling technique was calibrated using turbine and site-specific performance data. The EPC reduced estimation error to about 3% in the aforementioned time series during very good wind conditions. By integrating statistical wind speed forecasting methods and site-specific EPCs, wind power forecasting and management can be significantly improved without compromising grid stability.

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Gaussian mixture models for site-specific wind turbine power curves

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650920931729 ◽

2020 ◽

pp. 095765092093172

Author(s):

Bruno Srbinovski ◽

Andriy Temko ◽

Paul Leahy ◽

Vikram Pakrashi ◽

Emanuel Popovici

Keyword(s):

Wind Turbine ◽

Power Output ◽

Learning Algorithm ◽

Probabilistic Method ◽

Wind Farms ◽

Gaussian Mixture ◽

Model Parameters ◽

Power Curve ◽

Site Specific ◽

Power Curves

A probabilistic method for modelling empirical site-specific wind turbine power curves is proposed in this paper. The method is based on the Gaussian mixture model machine learning algorithm. Unlike standard wind turbine power curve models, it has a user-selectable number (N) and type of input features. The user can thus develop and test models with a combination of measured, derived or predicted input features relevant to wind turbine power-output performance. The proposed modelling approach is independent of the site location where the measurable input features (i.e. wind speed, wind direction, air density) are collected. However, the specific models are location and turbine dependent. An N-feature wind turbine power curve model developed with the proposed method allows us to accurately estimate or forecast the power output of a wind turbine for site-specific field conditions. All model parameters are selected using a k-fold cross-validation method. In this study, five models with different numbers and types of input features are tested for two different wind farms located in Ireland. The power forecast accuracy of the proposed models is compared against each other and with two benchmarks, parametric wind turbine power curve models. The most accurate models for each of the sites are identified.

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SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies

Applied Sciences ◽

10.3390/app10238685 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8685

Author(s):

Ravi Pandit ◽

Athanasios Kolios

Keyword(s):

Support Vector Machine ◽

Wind Turbine ◽

Condition Monitoring ◽

Offshore Wind ◽

Data Driven ◽

Accurate Estimation ◽

Support Vector ◽

Offshore Wind Turbine ◽

Power Curve ◽

Power Curves

Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.

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Adaptive filter–based power curve modeling to estimate wind turbine power output

Wind Engineering ◽

10.1177/0309524x19868427 ◽

2019 ◽

pp. 0309524X1986842

Author(s):

Bharti Dongre ◽

Rajesh Kumar Pateriya

Keyword(s):

Wind Turbine ◽

Adaptive Filter ◽

Power Output ◽

Learning Curves ◽

Least Square ◽

Power Curve ◽

Mean Square ◽

Least Mean Square ◽

Recursive Least Square ◽

Power Curves

This article presents a comparative study of adaptive filter–based power curve models to estimate wind turbine power output. In the real world, wind turbines are never subjected to ideal conditions; thus, adaptive filter–based power curves serve best when estimating the power in a time-varying environment. Adaptive filter–based power curve is implemented using various algorithms like least mean square, kernel least mean square, recursive least square, and kernel recursive least square algorithms. All models have been developed on National Renewable Energy Laboratory datasets. The performance of various models has been compared on the basis of parameters like mean absolute error, root mean square error, and R-squared score. In addition to this, the learning curves of each method have been obtained to show the performance variation over time.

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

Energies ◽

10.3390/en14041105 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1105 ◽

Cited By ~ 2

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.

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