From Lidar Measurement to Rotor Effective Wind Speed Prediction: Empirical Mode Decomposition and Gated Recurrent Unit Solution

Abstract

Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD–GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD–GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

“…Decomposition-based methods can be broadly categorized into two types. One generates all subsequences at the same hierarchical level, including variational mode decomposition (VMD) [49], adaptive VMD (AVMD), optimal VMD (OVMD) [50], local mean decomposition (LMD), empirical mode decomposition (EMD) [51], ensemble EMD (EEMD) [52], fast EEMD (FEEMD), complete EEMD (CEEMD) [53], CEEMD with adaptive noise (CEEMDAN), improved CEEMDAN (ICEEM-DAN) [54], singular spectrum analysis (SSA), improved SSA (ISSA) [55], and symmetric geometry mode decomposition (SGMD). The VMD itself, as well as AVMD and OVMD, demonstrate excellent performance in handling nonlinear and non-stationary signals.…”