Multi-modal regression analysis based hydroelectric generating set's cavitation erosion signal feature extraction method

A regression analysis and signal feature technology, which is applied in the recognition of patterns in signals, computer components, electrical and digital data processing, etc. Influence, well-characterized effect

Inactive Publication Date: 2017-02-15
CHINA THREE GORGES UNIV
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Problems solved by technology

Among them, the short-time Fourier transform is based on the assumption of piecewise stationary, once the signal does not meet the assumption, its analysis accuracy will be difficult to guarantee; the Wigner-Ville distribution has a high time-frequency resolution, and the time-frequency aggregation is relatively good , but when performing multi-component signal analysis, cross-interference items will be generated, which greatly restricts

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  • Multi-modal regression analysis based hydroelectric generating set's cavitation erosion signal feature extraction method
  • Multi-modal regression analysis based hydroelectric generating set's cavitation erosion signal feature extraction method
  • Multi-modal regression analysis based hydroelectric generating set's cavitation erosion signal feature extraction method

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Embodiment

[0074] The following is illustrated by the example data of cavitation signal of a large hydropower unit. The sampling rate of the high-speed acquisition module of the cavitation monitoring system is 1MHz, and the sampling range of the acoustic emission sensor is 50KHz-400KHz. The monitoring conditions include three working conditions: turbine idling, 30% opening and full-load operation. The time-domain waveform and power spectrum of the cavitation signal under the three working conditions are as follows: Figures 2 to 4 shown. It can be seen from the figure that in the time domain, the DC amplitudes of the cavitation signals under the three working conditions are not much different, and the overall waveform is irregular and it is difficult to obtain useful information. It can be seen from the power spectrum that the characteristic frequencies of the cavitation signal are concentrated around 75kHz and 175kHz, and the amplitude of the power spectrum varies significantly with th...

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Abstract

The invention relates to a multi-modal regression analysis based hydroelectric generating set's cavitation erosion signal feature extraction method, which comprises the following steps: 1) obtaining the cavitation erosion signal measuring data from a monitoring system; 2) establishing variation modal decomposition (VMD) parameters and decomposing the cavitation erosion signal into a series of modal components; 3) carrying out multivariate autoregressive modeling to all components; and using the Bayesian criterion to determine the order of the model; 4) constructing the model parameters identified by the QR decomposition method for the initial feature of the cavitation signal; and 5) using the principal component analysis to extract the main element for the final signal characteristics. The technical scheme of the invention realizes the adaptive separation of the intrinsic mode of the cavitation signal, and the characteristic vector obtained by the multimodal regression analysis embodies the important information of the system state, and realizes the full representation of the cavitation signal.

Description

technical field [0001] The invention belongs to the field of state monitoring and signal analysis of a hydroelectric unit in an electric power system, and in particular relates to a method for extracting the cavitation signal feature of a hydroelectric unit based on multimodal regression analysis. Background technique [0002] As the key equipment for hydroelectric energy conversion, hydroelectric units are constantly developing in the direction of complexity and giantization, and the coupling effect between various components is more intense, which brings about the continuous increase of nonlinearity and non-stationarity of unit operating signals, especially faults. The mapping relationship with symptoms is more complicated. In this regard, traditional condition monitoring and analysis methods have been difficult to meet the needs of hydropower unit operation analysis in the new situation, and there is an urgent need to study new theories and methods, such as exploring New...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06F17/50
CPCG06F30/3312G06F30/17G06F2218/04G06F2218/08G06F18/2135
Inventor 付文龙李玥桦
Owner CHINA THREE GORGES UNIV
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