Offshore Wind Turbine Harmonic Response Extraction via Frequency Segmentation
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Solution Overview
Problem
Existing methods for extracting harmonic responses from offshore wind turbines face challenges such as ignoring small harmonic energy as noise, failing to separate harmonic frequencies close to structural frequencies, and lacking utilization of physical information like rotational frequency.
Innovation Solution
A method utilizing deep generative models to extract harmonic responses by collecting and processing acceleration response signals from offshore wind turbines, employing Fourier transforms and deep learning to separate harmonic excitation from environmental loads, even when frequencies are closely aligned.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time-varying signal decomposition methods (wavelet transform, Hilbert-Huang transform, variational mode decomposition) are used to extract signal components, then the extraction process can be performed, but small harmonic energy is easily ignored as noise
Solution Approach 1:
The method performs preliminary segmentation of the frequency spectrum into multiple bands before analysis, identifying which segments contain harmonic excitation effects. This preliminary classification allows the system to focus computational resources on relevant frequency segments and avoid treating small harmonic signals as noise during the decomposition process
Solution Approach 2:
The method transforms the problem from time-domain signal decomposition to frequency-domain analysis by using Fourier transform. By changing the parameter domain from time to frequency, the system can clearly identify and extract harmonic components based on their frequency characteristics, preventing them from being mistaken for noise
2Measurement precision
If traditional decomposition methods are used, then signal components can be extracted, but harmonic frequency cannot be effectively separated from actual structural frequency when they are extremely close
Solution Approach 1:
The frequency spectrum is segmented into multiple frequency bands, with specific segments designated for harmonic excitation analysis. By dividing the spectrum and identifying segments containing harmonic effects, the method can isolate and analyze harmonic frequencies even when they are extremely close to structural frequencies, enabling effective separation through targeted frequency band analysis
Solution Approach 2:
The method uses frequency spectrum analysis as an intermediary step between raw signal acquisition and harmonic extraction. By introducing frequency domain transformation as an intermediate process, the system can clearly distinguish between harmonic and structural frequencies even when they are closely aligned in the time domain
3Productivity
If purely data-driven methods are used for component extraction, then the extraction can be performed, but known physical information (such as rotational frequency from SCADA system) cannot be effectively utilized
Solution Approach 1:
The method merges data-driven frequency spectrum analysis with physics-informed knowledge about harmonic excitation frequencies. By combining the computational power of spectral analysis with prior knowledge of where harmonic frequencies should appear (based on rotational speed and blade count), the system effectively utilizes both data and physical information to improve extraction efficiency and accuracy
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
Effectively separates harmonic excitation from environmental loads, even when frequencies are closely aligned, and utilizes physical information to enhance the accuracy of harmonic response extraction.
Implementation Method 1
By installing accelerometer sensors on the tower, vibrations of the wind turbine tower caused by the combined effects from environmental and harmonic excitation loads can be captured
Implementation Method 2
Sub-step F2, using Fourier transform to convert all signal segments from time domain to frequency domain, and obtaining the frequency spectrum P of the signals
Data Source
AI summary
A method for extracting harmonic response from offshore wind turbine structure, including: F1, continuously collecting its acceleration response under non-operating conditions, obtaining the acceleration response signal of the tower under only environmental load, and cropping all collected signals into signal segments with a length of L; F2, using Fourier transform to convert all signal segments from the time domain to the frequency domain, obtaining the frequency spectrum of the signal P; F3, according to the maximum rotational speed P, either designed or recorded by the SCADA system, determining the maximum harmonic response frequency Fmax=N×P, 1≤N≤12 to be extracted, and cropping the individual frequency spectrum into three segments P1, P2, P3, corresponding to frequency ranges of 0˜F1, F1˜F2, F2˜F3, where the selected frequency F1 is not less than Fmax; F4, cropping all frequency spectra according to F3 to form a frequency spectral dataset D without harmonic excitation effects.