# Height Control System for Wind Turbines Based on Critical Wind Speed Calculation
## 2 Related Work and State of the Art
It highlights that integrating advanced monitoring and control technologies can enhance turbine stability and reduce the risk of failure under variable weather conditions. These technologies include using sensorless control methods to minimize hardware costs and employing real-time data processing techniques to detect and respond to load changes in turbine components. The study published in [18] focuses on control strategies for vertical-axis wind turbines (VAWTs) and highlights the impact of active control systems on enhancing turbine efficiency. Implementing adaptive control techniques allows for real-time adjustments to varying wind conditions, which is crucial for improving the overall system performance and reliability. Such approaches are particularly beneficial for VAWTs like the Onipko rotor, as they reduce structural stress and improve the lifespan of key components [19]. This research demonstrates that optimizing control strategies rather than relying on purely aerodynamic design changes can be more effective in improving turbine efficiency and operational stability. The sensorless wind speed and rotor position control method presented in [20] reduces hardware costs and increases system reliability. By eliminating the need for additional physical sensors to measure wind speed and rotor position, this method decreases the overall cost of the turbine system. Instead, it relies on the output data from the rotor current controller and uses machine learning methods, such as particle swarm optimization and support vector regression, to estimate these parameters. This approach not only reduces the number of components needed but also minimizes maintenance and potential points of failure, thereby enhancing the system's reliability. The maximum power point tracking (MPPT) method improves energy conversion efficiency and reduces mechanical damage to wind turbines [21]. The study uses Fokker-Planck-Kolmogorov equations and the least squares method for the accurate tracking of the rotor speed probability density function shape, increasing power capture and reducing the frequency of system variable oscillations. While MPPT can significantly enhance wind turbine efficiency by optimizing power output, its application comes with certain limitations. The complex algorithms involved require high computational resources and powerful hardware, which increase system costs and complicate setup. Unlike MPPT, PID controllers are simple to implement, do not demand extensive computational power, and easily integrate into various systems. This simplicity makes PID controllers a more preferred choice in situations where reliability, cost-effectiveness, and ease of use are crucial [22]. These studies highlight the critical importance of integrating advanced technologies and control methods to enhance the efficiency and reliability of wind turbines. This paper presents a control method that effectively addresses challenges such as high computational demands and the complexity of mathematical models. Unlike other approaches that require significant resources for data processing and complex algorithms, the proposed method leverages simpler yet highly effective algorithms that maintain a high level of system performance. This approach allows for the implementation of solutions in resource-constrained environments while ensuring the reliability and stability of the system's operation.