Method for predicting vibration trend of hydroelectric generating set

Abstract

The invention relates to a hydroelectric generating set vibration trend prediction method. The method comprises the steps of obtaining a sample data set of hydroelectric generating set vibration monitoring data, analyzing the vibration monitoring data, drawing a relation coordinate graph of a to-be-predicted vibration parameter or throw parameter and other related parameters, and determining candidate characteristic parameters; a CMI-SAL prediction model is established; and screening unit key equipment vibration related working condition parameters through a condition mutual information method module. The method has the beneficial effects that the input characteristics are screened by applying a conditional mutual information correlation analysis method, two or more vibration variables can be analyzed, the relevance and correlation among the variables can be judged, the over-redundancy defect is overcome on the basis of a mutual information method, and the prediction efficiency is improved; by applying a sliding window and a maximum pooling method and adopting a convolutional layer, the order of magnitude scale of input data can be reduced, a local maximum value is extracted from input features, the number of trainable parameters is reduced, and the data robustness and the operation speed of the model are improved.

Description

technical field [0001] The invention belongs to the field of safety state detection and sequence prediction of hydropower stations, and in particular relates to a method for predicting vibration and swing of hydropower units based on a conditional mutual information method, a self-attention mechanism, and a long-term and short-term memory network. Background technique [0002] The hydro-generating unit (HGU) is an important equipment for hydropower generation, and its safe and stable operation is closely related to the normal operation of the hydropower station. With the increasingly complex HGU structure, the unit capacity and specific speed are gradually increasing, which puts forward higher requirements for ensuring the safe operation of hydroelectric generating units. During condition-based maintenance, by reflecting the attributes of equipment status and historical time-series data of operation, it can predict the development trend of the unit’s operating status in the ...

AI Technical Summary

Problems solved by technology

At present, the more common deep learning models are convolutional neural network, recurrent neural network, etc. However, due to the complexity of hydropower unit data, the prediction model based on deep learning algorithm has not yet completely solved the problem of prediction accuracy.

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