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

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...

AI Technical Summary

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 its application; wavelet transform has an adjustable time-frequency window, and is widely used in the feature extraction of rotating machinery faults, but there are difficulties in the selection of wavelet bases and The problem of poor self-adaptability; the empirical mode decomposition has the problem of endpoint effect and modal aliasing, which makes it difficult for the extracted features to fully reveal the original fault information

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