White Noise Characteristics and ARMA Model for Wind Speed Simulation

This article presents a methodology for generating random wind speed sequences using white noise characteristics and Autoregressive Moving Average (ARMA) models. This approach enables comprehensive analysis of wind turbine performance under varying wind conditions, ultimately contributing to improved wind power generation efficiency. The implementation involves solving ARMA model parameters through techniques like the Yule-Walker equations or maximum likelihood estimation, where white noise serves as the fundamental innovation process ensuring model accuracy. Key implementation aspects include: determining optimal model orders (p,q) using information criteria (AIC/BIC), parameter estimation algorithms, and generating realistic wind speed variations through recursive filtering operations. The technical discussion covers appropriate model order selection methodologies and validation techniques using white noise properties to maintain statistical consistency. Through this examination, readers will gain deep insights into wind power fundamentals and simulation techniques applicable to practical engineering scenarios, including code implementation strategies for ARMA model fitting and stochastic wind field generation.