Alternative Moment Method for wind energy potential and turbine energy output estimation. (May 2018)

• Record Type: Journal Article
• Authors: Akdağ, Seyit Ahmet; Güler, Önder
• Is Part Of: Renewable energy. Volume 120(2018)
• Page Start: 69
• Page End: 77
• Publication Date: 2018-05
• Subjects: Wind energy -- Wind speed distribution -- Weibull distribution -- Alternative Moment Method; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2017.12.072
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: An accurate analysis of wind characteristics is a critical factor for wind farm energy output estimation. Therefore, it is necessary to develop an effective and efficient method to asses wind energy resource. The main objective of the present paper is to introduce a novel method to estimate Weibull distribution parameters. This method is called Alternative Moment Method (AMM). AMM method is expressed in an analytical form and doesn't need iterative procedure. The efficiency and accuracy of the introduced method is compared with commonly used parameter estimation methods. Result of the graphical comparison showed that AMM method is better than Justus Moment Method (JMM) and Novel Energy Pattern Factor Method (NEPFM). While mean error of AMM was 3.53 × 10 −7 %, JMM and NEPFM was 0.63% and 0.0031% respectively. AMM method is validated by comparing wind turbine energy output estimation accuracy for 144 cases. AMM mean energy output estimation error for 750 kW and 1600 kW wind turbine was 0.039% and 0.043%, respectively. Moreover, data from 16 weather station distributed around Spain is used to evaluate the power density estimation capability of developed method. Result showed that mean absolute error of AMM and MLH was 3.47% and 10.59%, respectively. Highlights: Weibull distribution parameter estimation method is introduced to model wind speed. Fitting accuracy of introduced method is showed for 16 station and 144 cases. Introduced method correctly estimates wind turbine energy output and power density.