A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades

Computational fluid dynamics (CFD) is a powerful tool to estimate accurately the aerodynamic loads on wind turbine blades at the expense of high requirements like the duration of computation. Such requirements grow in the case of blade shape optimization in which several analyses are needed. A fast and reliable way to mimic the CFD solutions is to use surrogate models. In this study, a machine learning technique, the support vector regression (SVR) method based on a set of CFD solutions, is used as the surrogate model. CFD solutions are calculated by solving the Reynolds-averaged Navier–Stokes equation with the k-epsilon turbulence model using a commercial solver. The support vector regression model is then trained to give a functional relationship between the spanwise twist distribution and the generated torque. The smooth twist distribution is defined using a three-node cubic spline with four parameters in total. The optimum twist is determined for two baseline blade cases: the National Renewable Energy Laboratory (NREL) Phase II and Phase VI rotor blades. In the optimization process, extremum points that give the maximum torque are easily determined since the SVR gives an analytical model. Results show that it is possible to increase the torque generated by the NREL VI blade more than 10% just by redistributing the spanwise twist without carrying out a full geometry optimization of the blade shape with many shape-defining parameters. The increase in torque for the NREL II case is much higher.

Download Full-text

Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Energies ◽

10.3390/en12173396 ◽

2019 ◽

Vol 12 (17) ◽

pp. 3396 ◽

Cited By ~ 2

Author(s):

Mingzhu Tang ◽

Wei Chen ◽

Qi Zhao ◽

Huawei Wu ◽

Wen Long ◽

...

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Support Vector Regression ◽

Confidence Intervals ◽

Wind Turbines ◽

Large Scale ◽

Support Vector ◽

Effective Response ◽

Support Vector Regression Model ◽

Doubly Fed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.

Download Full-text

Wind Turbine Blade Damage Detection Using Various Machine Learning Algorithms

Volume 8: 28th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2016-59686 ◽

2016 ◽

Cited By ~ 4

Author(s):

Taylor Regan ◽

Rukiye Canturk ◽

Elizabeth Slavkovsky ◽

Christopher Niezrecki ◽

Murat Inalpolat

Keyword(s):

Machine Learning ◽

Damage Detection ◽

Wind Turbine ◽

Health Monitoring ◽

Turbine Blades ◽

Machine Learning Algorithms ◽

Support Vector ◽

Test Rig ◽

Wind Turbine Blades ◽

Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades (each fit with an internally located speaker and microphone), of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.

Download Full-text

Online faults diagnosis of wind turbine blades based on support vector machines

2016 3rd International Conference on Systems and Informatics (ICSAI) ◽

10.1109/icsai.2016.7810962 ◽

2016 ◽

Author(s):

Sun Qiang ◽

Han Lin ◽

Zhang Yu

Keyword(s):

Support Vector Machines ◽

Wind Turbine ◽

Turbine Blades ◽

Support Vector ◽

Wind Turbine Blades ◽

Vector Machines ◽

Faults Diagnosis

Download Full-text

Nonlinear Stress Analysis of Vertical-Axis Wind Turbine Blades

Journal of Engineering for Industry ◽

10.1115/1.3438734 ◽

1975 ◽

Vol 97 (4) ◽

pp. 1234-1237 ◽

Cited By ~ 1

Author(s):

L. I. Weingarten ◽

R. E. Nickell

Keyword(s):

Wind Turbine ◽

Turbine Blades ◽

Vertical Axis ◽

General Purpose ◽

Analytical Models ◽

Efficient Design ◽

Vertical Axis Wind Turbine ◽

Wind Turbine Blades ◽

Nonlinear Stress ◽

Blade Shape

A Darrieus-type vertical-axis wind turbine has been proposed as an alternate to conventional horizontal axis, propeller-type machines. An advantage is that the blades will be primarily in tension, thus making for a more efficient design. In connection with its vertical-axis wind turbine program, Sandia Laboratories has developed the “troposkien” (Greek for turning rope) shape for the blade design. The prototype blade shape is similar to that of the troposkien, but more easily manufactured. The effect on the stress distribution of this alternate blade shape is investigated. Analytical models of the blades were constructed using a general purpose, nonlinear, dynamic finite element structural code. This code accounts for the large deflection effects of the various blade shapes through incremental applications of angular velocity.

Download Full-text