RETRACTED ARTICLE: Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction

• Published in: Stochastic environmental research and risk assessment Vol. 29; no. 3; pp. 793 - 802
• Main Authors: Petković, Dalibor, Shamshirband, Shahaboddin, Anuar, Nor Badrul, Naji, Sareh, Mat Kiah, Miss Laiha, Gani, Abdullah
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2015; Springer Nature B.V
• Subjects: Adequacy; Algorithms; Aquatic Pollution; Bearing; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Environment; Frequency distribution; Fuzzy logic; Fuzzy systems; Inference; Math. Appl. in Environmental Science; Neural networks; Original Paper; Parameter modification; Physics; Probability Theory and Stochastic Processes; Statistics for Engineering; Turbines; Velocity; Vigor; Waste Water Technology; Water Management; Water Pollution Control; Wind direction; Wind farms; Wind power; Wind power generation; Wind speed; Wind turbines; Wind direction; Wind energy; Wind speed; Combined forecasting; Adaptive neuro-fuzzy inference system (ANFIS)
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-014-0901-8

Wind velocity assumes a critical part for measuring the power created by the wind turbines. Nonetheless, power production from wind has a few weaknesses. One significant issue is that wind is a discontinuous energy source which implies that there exists substantial variability in the generation of vigor because of different variables, for example, wind speed. Wind direction is a significant variable for proficient turbine control for getting the most energy with a given wind speed. Taking into account the conjectures on wind heading, it might be conceivable to adjust the turbine to the wind bearing to get the most energy yield. Since both forecasts of wind speed and direction are basic for effective wind energy collecting it is crucial to develop a methodology for estimation of wind speed and direction and afterwards to estimate wind farm power production as function of wind pace and heading distribution. Despite the fact that various numerical functions have been proposed for demonstrating the wind speed and direction frequency distribution, there are still disadvantages of the models like very demanding in terms of calculation time. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a particular sort of the artificial neural networks (ANN) family, was used to anticipate the wind speed and direction frequency dispersion. Thereafter, the ANFIS system was utilized to gauge wind homestead power creation as function of wind velocity and bearing. Neural system in ANFIS modifies parameters of enrollment capacity in the fuzzy logic of the fuzzy inference system. The reenactment outcomes exhibited in this paper demonstrate the adequacy of the created technique.