A novel intelligent approach for yaw position forecasting in wind energy systems. (July 2015)

• Record Type: Journal Article
• Authors: Yesilbudak, Mehmet; Sagiroglu, Seref; Colak, Ilhami
• Is Part Of: International journal of electrical power & energy systems. Volume 69(2015:Jul.)
• Page Start: 406
• Page End: 413
• Publication Date: 2015-07
• Subjects: Yaw position -- Wind turbines -- Forecasting -- Lazy learning -- Multi-tupled inputs; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.01.030
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The yaw position parameter of a wind turbine is forecasted using multi-tupled inputs. The developed k-NN classifier leads to improve the wind turbine efficiency. Many useful and reasonable outcomes unmentioned in the literature were uncovered. Abstract: Yaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k = 3 uncovered the most accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k = 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent.