A Method and System for Wind Energy Resource Assessment in Wind Farms Based on Numerical Simulation

By constructing unit association pairs and performing linear programming, the dynamic wake expansion rate parameter is determined, which solves the problem of inaccurate assessment caused by fixed wake expansion rate parameters in existing technologies, and improves the accuracy and adaptability of wind farm energy resource assessment.

CN121787336BActive Publication Date: 2026-05-26POWERCHINA HUADONG ENG CORP LTD +2
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
POWERCHINA HUADONG ENG CORP LTD
Filing Date
2026-03-05
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In existing technologies, wind farm wind energy resource assessments use fixed wake expansion rate parameters, which cannot accurately reflect the time-varying characteristics of atmospheric turbulence and the wake characteristics of high wind speed constant power operating areas, resulting in poor assessment accuracy and adaptability.

Method used

By acquiring the real-time yaw angle, active power, and blade pitch angle of all units in the wind farm, we construct unit correlation pairs and build a state transition resistance matrix. We then perform linear programming to determine the dynamic wake expansion rate parameters, remove the influence of geometric yaw, quantify the turbulent mixing capacity, and realize the wind energy resource assessment of the wind farm.

Benefits of technology

It improves the accuracy of wind farm energy resource assessment, and can adaptively adjust the wake expansion rate parameter according to the atmospheric turbulence state, accurately reflecting the wake characteristics and improving the accuracy of the assessment.

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Abstract

This invention relates to the field of wind farm resource management technology, specifically to a method and system for assessing wind farm energy resources based on numerical simulation. First, thrust state indicators are determined based on active power, generator speed, and blade pitch angle. Pitch angle is introduced to compensate for the saturation effect of active power signals in high wind speed ranges, effectively extracting wake characteristics across the entire wind speed range. Further, a state transition drag matrix is ​​constructed based on the distribution deviation of thrust state indicators between upstream and downstream units. Linear programming is then used to minimize the total transmission cost. The constraint effect of the drag matrix eliminates the interference of geometric yaw on wake recovery observations, quantifying the true atmospheric turbulence mixing capacity. Finally, based on minimizing the total transmission cost, a more accurate dynamic wake dilatation rate parameter is dynamically determined, resulting in higher accuracy in assessing wind farm energy resources based on the dynamic wake dilatation rate parameter.
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