Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter

• Published in: Applied sciences Vol. 14; no. 19; p. 8764
• Main Authors: Kim, Dongmyoung, Jeon, Taesu, Paek, Insu, Roynarin, Wirachai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Air-turbines; Algorithms; Analysis; available power; Control algorithms; Costs (Law); Differential equations; Dynamic link libraries; feed-forward control; Fuzzy logic; Methods; Simulation; Standard deviation; Turbines; Wind farms; wind speed estimation; wind turbine; South Korea
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14198764

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.