Intelligent prediction method for power generation of wind farm based on high-precision CFD simulation

By combining high-precision CFD simulation and hierarchical dimensionality reduction with multi-scenario simulation verification, the adaptability and accuracy of the wind farm frequency domain equivalent model under complex operating scenarios were solved. This enabled the efficient construction and stable operation of the wind farm frequency domain equivalent model, thereby improving the analytical capabilities of the power system.

CN122263703APending Publication Date: 2026-06-23TIANJIN HUIFENG ENG DESIGN CONSULTING
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TIANJIN HUIFENG ENG DESIGN CONSULTING
Filing Date
2026-02-03
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing wind farm frequency domain equivalent models are difficult to accurately extract key data features during the construction process and lack consideration for complex operating scenarios. As a result, the models are not adaptable and accurate enough when facing different operating conditions, data fluctuations or fault scenarios, and cannot meet the needs of rapid analysis of power systems.

Method used

A smart prediction method for wind farm power generation based on high-precision CFD simulation is adopted. Through data acquisition and preparation, time-domain data frequency-domain conversion, parameter identification and model construction, hierarchical dimensionality reduction processing and multi-scenario simulation verification, a frequency-domain equivalent model of wind farm is constructed. Dimensionality reduction processing is performed by combining principal component analysis and wavelet packet decomposition. Parameters are optimized by combining genetic algorithm and least squares method. Various operating scenarios are constructed for model verification.

Benefits of technology

It significantly improves model building efficiency, enhances the model's adaptability and generalization ability under complex and variable operating conditions, ensures that the model maintains stable and accurate performance under different operating conditions, and provides more stable and reliable technical support.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122263703A_ABST
    Figure CN122263703A_ABST
Patent Text Reader

Abstract

The application discloses a wind farm power generation intelligent prediction method based on high-precision CFD simulation, and comprises the following steps: S1, data acquisition and preparation; S2, time domain data frequency domain conversion; S3, parameter identification and model construction; S4, hierarchical dimension reduction processing; and S5, multi-scenario simulation verification.The application uses the method that combines principal component analysis and wavelet packet decomposition to perform hierarchical dimension reduction on the frequency domain data through hierarchical dimension reduction processing.The operation can accurately extract main characteristic components of data, remove redundant information, and map high-dimensional data to a low-dimensional space.Under the condition of ensuring that key information is not lost, the calculation amount of subsequent parameter identification and model construction is greatly reduced, the model construction time is effectively shortened, the model construction efficiency is significantly improved, and the power system analysis process is accelerated.
Need to check novelty before this filing date? Find Prior Art