A novel decomposition-based approach for non-stationary hub-height wind speed modelling

An accurate description of hub-height wind speed characteristics is indispensable to offshore wind resource assessment and structure reliability analysis. However, given the assumption of stationarity in wind speeds that is violated, the commonly used stationary statistical models will lead to bias, and a non-stationary frequency analysis is required. In this paper, a novel decomposition-based non-stationary modelling approach was proposed. To decompose time series into deterministic and stochastic components, a procedure was designed by combining signal decomposition methods and recurrence quantification analysis and the performances of seven signal decomposition methods were evaluated under various represent non-stationary scenarios via numerical experiments. Then the non-stationary model was established by aggregating the modelled two components. Compared with other methods, the proposed approach is superior, which is of good self-adaption to data and relies on no hypotheses and explanatory covariates, guaranteeing the simplicity and reliability of the constructed models. Additionally, the SSA-based procedure is capable of capturing complicated non-stationary patterns while preserving the higher-order moments of the underlying stochastic process. The capacity of the proposed approach was verified using wind speed data at six positions distributed along China's coastline. Results emphasize the importance of the consideration of non-stationarity and the necessity of this study.