Water turbine rotating shaft state monitoring method and system based on a neural network

Abstract

The invention relates to a water turbine rotating shaft state monitoring method and system based on a neural network. The method comprises the following steps: 1) establishing and training a neural network-based rotating shaft temperature prediction model according to historical data of the rotating shaft of the water turbine; 2) determining a fault threshold of the rotating shaft of the water turbine through a nuclear density estimation method; and 3) predicting the temperature residual distribution characteristic of the rotating shaft through the trained rotating shaft temperature predictionmodel by using the real-time data of the rotating shaft of the water turbine, and comparing the rotating shaft temperature residual distribution characteristic with the fault threshold to judge whether a fault occurs or not. According to the method, a correlation coefficient method is used for selecting parameters having certain correlation with the rotating shaft temperature for modeling, invalid values in data are removed before all correlation coefficients are calculated, smoothing and normalization processing are carried out, and an exponential weighting moving average filter is used forcarrying out smoothing processing on a residual sequence. According to the method, prediction of potential faults of the rotating shaft can be effectively achieved, and the negative effect of seriousdestructive accidents of a unit is prevented.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence and hydroelectric power generation, and relates to a monitoring method, in particular to a neural network-based method and system for monitoring the state of a rotating shaft of a water turbine. Background technique [0002] With the continuous development of my country's electric power industry, hydropower energy has entered a new era of large units, UHV transmission, and intelligent control and management. On the one hand, the structural proportion of hydropower in my country's power generation supply segment is gradually increasing, and more tasks of peak regulation and frequency regulation are undertaken. As the key power generation equipment of hydropower plants, water turbines are also developing in the direction of large capacity, high specific speed, and high load. The structure and layout of its components are becoming more and more complex. The coupling effect between them is bec...

AI Technical Summary

Problems solved by technology

As the key power generation equipment of hydropower plants, water turbines are also developing in the direction of large capacity, high specific speed, and high load. The structure and layout of its components are becoming more and more complex. The coupling effect among them has become increasingly prominent, which has led to an increasing number of unplanned outages and sudden impact failures of hydraulic turbine failures, and has directly affected the safety and stability of the power grid.

[0003] Therefore, the traditional state assessment and fault diagnosis methods of turbine shafts cannot meet the actual engineering needs of current hydropower plants for rapid fault location and timely processing.

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