# Hierarchical Data Fusion Algorithm for Multiple Wind Speed Sensors in Anemometer Tower

## TL;DR

This paper introduces a two-stage data fusion algorithm combining advanced filtering and machine learning to improve wind speed measurements from multiple sensors on anemometer towers.

## Contribution

A novel hierarchical data fusion framework using FLR-UKF and QLIAO-ELM is proposed for enhanced wind speed sensor fusion.

## Key findings

- FLR-UKF reduces RMSE by 26.46% to 28.6% compared to traditional UKF in local fusion.
- QLIAO-ELM outperforms ELM and ISSA-ELM with 27.1% and 14.0% RMSE reduction in global fusion.
- The proposed method improves both accuracy and computational efficiency in wind speed data fusion.

## Abstract

Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-precision wind speed information. In this study, a hierarchical data fusion strategy is proposed to enhance both the quality and efficiency of multi-sensor fusion on wind measurement towers. At the local fusion stage, multi-sensor wind speed data are denoised and fused using an unscented Kalman filter enhanced with fuzzy logic and a robustness factor (FLR-UKF). At the global decision fusion stage, decision-level fusion is achieved through an extreme learning machine (ELM) neural network optimized by a Q-learning-improved Aquila optimizer (QLIAO-ELM). By incorporating a spiral surrounding attack mechanism and a Q-learning-based adaptive strategy, QLIAO-ELM significantly enhances global search capability and convergence speed, enabling the ELM network to obtain superior parameters within limited computational time. Consequently, the accuracy and efficiency of decision fusion are improved. Experimental results show that, during the local fusion phase, the RMSE of FLR-UKF is reduced by 26.46% to 28.6% compared to the traditional UKF; during the global fusion phase, the RMSE of QLIAO-ELM is reduced by 27.1% and 14.0% compared to ELM and ISSA-ELM, respectively.