Wind Speed Estimation by Hybrid Vector Reconstruction
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Solution Overview
Problem
Existing methods for estimating wind speed from lidar, radar, or sodar systems under conditions of strong atmospheric turbulence result in significant errors, exceeding 4% relative error compared to cup anemometer measurements, and are sensitive to atmospheric conditions.
Innovation Solution
A method involving hybridization by temporal combination and weighting of wind speed vector components, using equations to reconstruct average wind speed values, which includes steps of vector and scalar reconstruction, and a weighting factor to minimize errors across varying atmospheric conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the scalar method or vector method is used to estimate wind speed from lidar, radar or sodar measurements, then the measurement process is simple, but under conditions of strong atmospheric turbulence the relative error may reach absolute values of 4%
Solution Approach 1:
The measurement time interval is divided into multiple partition time intervals, and the wind speed vector components are reconstructed for each partition interval separately. This segmentation allows the method to capture temporal variations in wind conditions more effectively, reducing estimation errors under strong atmospheric turbulence while maintaining a systematic and manageable processing framework.
Solution Approach 2:
The invention transitions from scalar or simple vector methods to a multi-dimensional approach by reconstructing multiple components of the wind speed vector (horizontal and vertical components) across multiple time partitions. This dimensional expansion enables more comprehensive characterization of wind field structure, improving measurement precision without excessive complexity increase.
2Measurement precision
If the scalar method or vector method is used to estimate wind speed, then the calculation process is straightforward, but the estimates differ markedly from the real wind speed under conditions of strong atmospheric turbulence
Solution Approach 1:
The method performs preliminary reconstruction of wind speed vector components for each partition time interval before combining them to obtain the final average wind speed. This preliminary action at the component level allows for more accurate representation of turbulent variations, improving precision while the systematic combination process maintains computational efficiency.
Solution Approach 2:
The invention maintains continuous reconstruction of wind speed vector components across multiple partition intervals, ensuring that no temporal information is lost. This continuous action through the measurement period captures the full dynamics of atmospheric turbulence, improving accuracy without requiring discontinuous or intermittent measurements that would reduce productivity.
3Measurement precision
If standard reconstruction methods are used, then the method is easy to implement, but the error of wind speed values determined reaches absolute values of 4% under conditions of strong atmospheric turbulence
Solution Approach 1:
The data processing is segmented into distinct steps: reconstructing horizontal components, reconstructing vertical components, and combining them to obtain average wind speed. This segmentation reduces the complexity of each individual processing step while achieving superior overall accuracy compared to standard methods that treat the entire reconstruction as a single operation.
Solution Approach 2:
The method dynamically adapts to varying atmospheric conditions by processing each partition time interval independently and combining results. This dynamic approach allows the system to maintain high precision under both strong and weak turbulence conditions, unlike static methods that are optimized for specific conditions only.
Data Source
AI summary
A method for processing telemetry data for estimating a wind speed. The method includes a hybridization by temporal combination, and/or by weighting, and/or by averaged projection.