Wind turbines operate under highly variable aerodynamic, structural, and environmental conditions, producing complex, nonlinear, and nonstationary signals. Traditional linear and spectral analysis approaches often fail to capture long-range dependencies, multiscale fluctuations, and intermittency that characterize wind speed, vibration, acoustic emission, and power output signals. Fractal theory—encompassing monofractal and multifractal scaling, Hurst exponent estimation, fractal dimension analysis, and complexity measures—has emerged as a powerful framework for analyzing these irregular signals. This review synthesizes the significant body of research applying fractal methods to wind turbine monitoring, forecasting, and control. We examine fractal-based approaches in wind resource characterization, turbine performance analysis, structural health monitoring, fault detection, and power output modeling. The review highlights the strengths and limitations of existing methods and identifies open research directions for integrating fractal features with machine learning, digital twins, and real-time control systems.
Namazi et al. (Tue,) studied this question.