Build-up an analysis model to evaluate wind potential for selecting suitable turbine configurations and proposing research to optimize wind farm design

Abstract

Before a wind farm is constructed, the assessment of wind potential in the proposed turbine installation area must be carried out as a prerequisite. To achieve the highest efficiency, the key concepts and a deep understanding of wind energy evaluation must be mastered by the design engineers. The article of study applies Weibull distribution theory and aerodynamics fundamental to build an analytical model for evaluating standards and estimating annual electricity output based on raw input data collected over a year. The calculation results for the wind characteristics, including shape and scale factor and power density at the surveyed area corresponding to an 80m height, are determined in detail by simulation software. The analysis results also indicate that the wind potential here is classified as very high (class 6), and a minimum II-A type turbine configuration must be selected to withstand these wind conditions.

Since the initial investment cost of a wind farm will be determined by the simulation results, the study aims to combine the calculation methods used in this research with the application of digital twin solutions and machine learning for wind farms to create an accurately digital replica of a physical system. In this way, real-time system operations will be monitored and continuously updated into the simulation model to understand and predict its behavior. From there, optimal design and operation adjustments will be made to enhance the overall system's efficiency and minimize errors and risks for investors.