Comprehensive Evaluation of Wind Turbine Type Selection Based on GA-SVR Model

2012 ◽  
Vol 468-471 ◽  
pp. 579-582
Author(s):  
Wei Sun ◽  
Le Shen
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Wind Turbine ◽  
Support Vector Regression ◽  
Empirical Analysis ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
High Accuracy ◽  
Support Vector ◽  
Type Selection

Aiming at the current situation of wind turbine type selection in China, this paper has built a more scientific and systematic index system for comprehensive evaluation of wind turbine type selection, and also applied the Support Vector Regression machine evaluation model with parameters optimized by Genetic Algorithm. Through automatic global optimization for parameters, this model has reached an extremely high accuracy required for evaluation of type selection. Empirical analysis shows that the application of this model has a realistic popularized significance for improving the method of the wind turbine type selection and enhancing its efficiency.

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

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3396 ◽  
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.

scholarly journals Applying hybrid support vector regression and genetic algorithm to water alternating CO 2 gas EOR

2020 ◽  
Vol 10 (3) ◽  
pp. 613-630 ◽  
Author(s):  
Menad Nait Amar ◽  
Noureddine Zeraibi ◽  
Ashkan Jahanbani Ghahfarokhi
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Support Vector

Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission

Energy ◽  
2014 ◽  
Vol 67 ◽  
pp. 623-630 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Dalibor Petković ◽  
Amineh Amini ◽  
Nor Badrul Anuar ◽  
Vlastimir Nikolić ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Support Vector Regression ◽  
Continuous Variable ◽  
Support Vector ◽  
Continuous Variable Transmission ◽  
Power Split ◽  
Variable Transmission ◽  
Reaction Torque
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