Design and Implementation of a Machine Learning-Based Wind Turbine Control System

Abstract

The Machine Learning-Based Wind Turbine Control System (MLBWTCS) is a new technology that uses machine learning algorithms to optimize the performance of wind turbines. The system collects data from sensors installed on the wind turbine to monitor various variables such as wind speed, blade pitch angle, generator torque, and power output. The data collected is preprocessed and fed into a machine learning model, which predicts the optimal settings for the turbine operations. The predictions are then used to control the operations of the wind turbine in real-time. The MLBWTCS has been shown to improve the efficiency and reliability of wind turbines, resulting in increased power generation and reduced maintenance costs. This paper presents a detailed description of the design and implementation of the MLBWTCS, including data collection, preprocessing, feature selection and machine learning model selection.

Keywords

• machine learning
• wind turbine control system
• preprocessing
• feature selection