A novel intelligent approach for yaw position forecasting in wind energy systems |
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| Affiliation: | 1. Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, 50300 Nevsehir, Turkey;2. Department of Computer Engineering, Faculty of Engineering, Gazi University, 06500 Ankara, Turkey;3. Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, 06500 Ankara, Turkey;4. Department of Mechatronic Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul, Turkey;1. Chemistry Division, School of Advanced Sciences, Vellore Institute of Technology (VIT), Chennai Campus, Vandalur – Kelambakkam Road, Chennai 600127, India;2. Materials Science Group, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, Tamil Nadu 603102, India;1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, PR China;2. School of Biological Sciences, Nanjing Normal University, Nanjing 210046, PR China;3. Philips Institute for Oral Health Research, Virginia Commonwealth University, 521 North 11th Street, Richmond, VA 23298, USA;1. Department of Civil Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China;2. Key Laboratory of New Technology for Construction of Cities in Mountain Area (Ministry of Education), School of Civil Engineering, Chongqing University, Chongqing, 400045, China |
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| 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. |
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| Keywords: | Yaw position Wind turbines Forecasting Lazy learning Multi-tupled inputs |
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