Hybrid Wind Speed Spectrum for Accurate Return Period Calculation
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Solution Overview
Problem
Existing methods for calculating return period wind speeds at proposed wind turbine sites rely on short-term local measurements, which are inadequate for long-term predictions, leading to inaccurate estimations of extreme wind speeds and potential underconfiguration of wind turbines.
Innovation Solution
A method combining measured wind speed data from a short-term period with modeled wind speeds from a mesoscale model, transforming both into a frequency domain, generating a hybrid spectrum, and then converting back to the time domain to calculate a more accurate return period wind speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If short-term local wind speed measurements are used to calculate return period wind speed, then the measurement process is simple and quick, but the accuracy of extreme wind speed estimation deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-generating modeled wind speed data covering an extended period (e.g., 50 years) using mesoscale models before the actual measurement period. This pre-computed data is then combined with actual short-term measurements, allowing the system to achieve long-term prediction accuracy without requiring long-term physical measurements. The modeled data serves as a head start, eliminating the need to wait for long measurement periods.
Solution Approach 2:
The patent merges two different data sources: actual short-term local wind speed measurements and modeled wind speed data from mesoscale models covering an extended period. By combining these datasets and transforming them into a hybrid spectrum in the frequency domain, the system achieves both the local accuracy of measurements and the temporal coverage of long-term models, resolving the contradiction between measurement duration and estimation accuracy.
2Reliability
If short-term measurements are used for long-term predictions, then the data collection period is short, but the reliability of return period wind speed calculation deteriorates
Solution Approach 1:
The patent introduces an intermediary approach by using frequency domain transformation and spectral combining as a bridge between short-term measurements and long-term predictions. The measured wind speed spectrum and modeled wind speed spectrum are combined in the frequency domain, creating a hybrid spectrum that acts as an intermediary representation. This hybrid spectrum then serves as a reliable basis for calculating return period wind speeds, bridging the gap between limited measurement duration and long-term prediction requirements.
3Measurement precision
If purely modeled wind speeds are used, then the extended period coverage is achieved, but the local site-specific accuracy deteriorates
Solution Approach 1:
The patent applies local quality by ensuring that the high-frequency portion of the hybrid spectrum (which represents local turbulence and site-specific characteristics) comes from actual local measurements, while the low-frequency portion (representing broader atmospheric patterns) comes from the modeled data. This frequency-based segmentation allows the system to preserve local site-specific accuracy in the relevant frequency ranges while still benefiting from the extended period coverage of modeled data, without requiring complex spatial interpolation methods.
Data Source
AI summary
A method of calculating a return period wind speed for a proposed wind turbine site is provided. Wind speed measurements and modeled wind speeds associated with the proposed site are provided. The measured and modeled wind speeds are transformed into the frequency domain, and combined to generate a hybrid spectrum. The hybrid spectrum is transformed into the time domain to generate a set of hybrid wind speed measurements, which are used to calculate the return period wind speed.