The Environment and Wind Energy Production by Analyzing Noise Filtering in Wind Signals to Improve the Efficiency of Energy Systems

Noise filtering is an important process for improving the accuracy and efficiency of signals captured by wind sensors, which are used to monitor and optimize the performance of wind-based energy systems. Wind signals often contain interference and noise, which can complicate analysis and decision-making related to energy production and turbine maintenance. In this context, noise filtering helps improve the quality of data collected by the sensors and allows for a more accurate assessment of wind speed and direction, contributing to more efficient energy management. This is crucial for optimizing energy production, reducing costs, and increasing the sustainability of energy systems. The use of signal filtering algorithms can significantly enhance the performance of energy systems by eliminating the negative impacts of external factors, such as acoustic pollution and interference from other sources. Noise in wind signals, caused by atmospheric disturbances, sensor inaccuracies, and electromagnetic interference, reduces the efficiency of energy systems. This study focuses on implementing digital filters to improve signal quality, thereby enhancing turbine performance. The implementation of FIR, IIR, wavelet transform, Kalman filters, and spectral analysis aims to optimize wind energy production.