Turbine back pressure trend prediction method based on catboost algorithm

Abstract

The invention discloses a turbine backpressure trend prediction method based on a catboost algorithm, and relates to the field of machine learning, and the method comprises the following steps: S1, preparing historical operation data, S2, carrying out the preprocessing of the historical operation data, S3, carrying out the feature engineering dimension raising of the preprocessing data, S4, carrying out the re-sampling and screening of variables having a large correlation degree with the backpressure, S5, determining parameters, carrying out the modeling through catboost, and carrying out the prediction of the backpressure trend of a turbine. The screened historical operation data predicts the back pressure in a certain period of time, and the model trained in the step S6 is used for predicting and replacing with the model trained offline when the error of the current training preparation model and the current prediction model is too large at the same time; the method is indirect and easy to operate, can effectively predict the back pressure of the steam turbine, enables the prediction model and offline training to be parallel, can make up the error of the prediction model in time, and improves the prediction precision.

Description

technical field [0001] The invention relates to the field of machine learning and belongs to the power plant operation and maintenance technology, in particular to a method for predicting the back pressure trend of a steam turbine based on a CatBoost algorithm. Background technique [0002] With the rapid development of the power industry, more and more large-scale units have been put into operation one after another. The increase in unit capacity has made the structure and system increasingly complex. How to ensure the safe and reliable operation of these units is of great importance to the development of the national economy. significance. [0003] In a sense, trend forecasting is a more urgently needed function for on-site production. Compared with fault diagnosis, on-site personnel are more concerned about what state the unit will continue to run in, hoping to predict future trends. Therefore, it is more important for enterprises to understand the future development tre...

AI Technical Summary

Problems solved by technology

[0006] In thermal power generation equipment, the steam turbine is an important equipment, and the maintenance of the steam turbine needs to be timely, and the influence of the back pressure of the steam turbine on the steam turbine is particularly important. However, the back pressure monitoring in the power plant can only be detected in real time to obtain the back pressure data. Once the pressure exceeds the expected steam turbine may have been damaged, if the change of the back pressure of the steam turbine can be predicted in advance, and the machine can be shut down for maintenance before the back pressure changes, the serious damage of the steam turbine can be avoided

However, in the prior art, there is almost no relevant technology for the prediction of the back pressure of the steam turbine.

Images