LiDAR Wind Component Reconstruction Using Kalman Filtering
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Solution Overview
Problem
Conventional methods for determining wind speed components, such as using measurement masts and LiDAR sensors, are costly, cumbersome, and provide incomplete or noisy measurements, necessitating improved processing to achieve accurate and reliable wind evaluations for wind turbine installations.
Innovation Solution
A method utilizing a placed LiDAR sensor oriented vertically to acquire measurement signals, which are approximated and filtered through a geometric reconstruction and a non-stationary Kalman filter to construct a wind signal model, enabling robust determination of wind speed components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a measurement mast with multiple sensors is used to determine wind speed components, then measurement accuracy is improved, but installation cost and device complexity increase significantly
Solution Approach 1:
The patent replaces the mechanical measurement mast system with multiple physical sensors with an optical LiDAR system that uses laser beams to measure wind speed components. The LiDAR sensor emits laser beams that scatter off atmospheric particles, and the Doppler shift in the scattered light provides wind speed information, eliminating the need for complex mechanical sensor arrays.
Solution Approach 2:
The patent uses optical copies (laser beams) to probe the wind field rather than physical contact with mechanical sensors. The laser beams create virtual measurement paths through the atmosphere, allowing wind speed determination at multiple points without physical structures.
2Ease of operation
If a LiDAR sensor is used to remotely measure wind, then installation cost and ease of operation are improved, but measurement precision deteriorates due to noise and incomplete data
Solution Approach 1:
The patent implements an iterative feedback process where initial wind speed approximations are obtained from LiDAR measurements, then used to construct a wind signal model that guides subsequent measurements and filtering operations. The system continuously refines the wind field reconstruction by comparing measured radial velocities with model predictions and adjusting accordingly.
Solution Approach 2:
The patent performs preliminary approximation of wind speed components before final reconstruction. Initial estimates are obtained from the LiDAR radial measurements, which then serve as input for constructing the wind signal model and guiding the filtering process to achieve more accurate final results.
3Loss of information
If multiple radial measurements are combined to reconstruct wind field, then completeness of wind information is improved, but processing complexity and time increase
Solution Approach 1:
The patent transforms the wind field representation by changing parameters from raw radial velocity measurements to approximated wind speed components, then to filtered measurement signals, and finally to reconstructed wind field. This parameter transformation sequence simplifies the processing by working with progressively refined representations.
Solution Approach 2:
The patent segments the wind field reconstruction process into distinct stages: obtaining radial measurements, approximating wind speed components, constructing wind signal model, filtering measurements, and final reconstruction. This segmentation allows each stage to be optimized independently and reduces overall processing complexity.
4Device complexity
If standard reconstruction methods are used assuming uniform wind field, then processing simplicity is improved, but measurement precision deteriorates due to unrealistic assumptions
Solution Approach 1:
The patent transitions from static assumptions of uniform wind field to a dynamic approach where the wind field is modeled as time-varying and spatially heterogeneous. The wind signal model incorporates temporal and spatial variations, allowing the system to adapt to changing wind conditions and provide more accurate representations of real atmospheric behavior.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, reliable, and cost-effective determination of wind speed components, allowing for efficient site analysis and wind turbine installation planning.
Implementation Method 1
LiDAR is a remote sensing or optical measurement technology based on analyzing the properties of a beam returned to its emitter. This method is used in particular to determine the distance to an object by way of a pulsed laser.
Implementation Method 2
the LiDAR sensor uses visible or infrared light rather than radio waves
Data Source
AI summary
The invention is a method for determining wind speed components by using a placed LiDAR sensor (1). For this method, the wind speed components are first approximated by using the signals from the LiDAR sensor (1). These approximations are used in a wind signal model, and then used in a non-stationary Kalman filter (KAL), to construct filtered measurement signals. The filtered measurement signals are then used to reconstruct (REC) the wind speed components.