Early warning method and device for NOx exceeding standard of gas turbine based on LSTM deep learning

Abstract

The invention belongs to the field of power plant safety control system, and particularly relates to a early warning method and a device for NOx exceeding standard of a gas turbine based on LSTM deeplearning. This patent lies in applying the LSTM prediction model to the NOx prediction of the gas turbine, as the NOx emissions need to be affected by a variety of factors, mainly factors such as flue gas temperature, differences in fuel gas, and oxygen content, therefore, it usually takes a while for these factors through a series of reactions to pass through related equipment and finally lead to NOx emissions. Through real-time model calculation, if it is found that the NOx content value reaches 50ppm, pollution prediction can be issued in advance, and emission reduction measures can be formulated in advance, so as to achieve intelligent monitoring of NOx emissions and early warning in advance. The LSTM method is used to process the raw data to adaptively find out the characteristic parameters associated with NOx emissions and avoid omissions or errors caused by manual selection of features by traditional machine learning methods. The invention has a large amount of collected data,small analysis errors, and high accuracy of early warning results.

Description

technical field [0001] The invention belongs to the field of power plant safety control systems, and in particular relates to a gas turbine NOx excessive early warning method and device based on LSTM deep learning. Background technique [0002] In the prior art, methods for detecting abnormalities of gas turbines can be roughly divided into two categories. One is the method of using a mechanism model to establish a physical model based on the thermodynamic properties and thermodynamic principles of the gas, and use the model to calculate various KPI indicators of the gas turbine, and compare them with the measured values. If there is a large deviation between the measured value and the theoretical value, it is considered that the gas turbine is abnormal. The main problem of the mechanism model is that when using the principles of physics to establish the analysis model, there are a lot of assumptions and simplified conditions, which are not suitable for complex systems in r...

AI Technical Summary

Problems solved by technology

Its shortcoming is that the selected characteristics are parameters that have obvious correlation with the NOx content of the output measuring point, and it is necessary to select key influencing factor characteristics for the input of the modeling, such as load, NOx inlet concentration, SCR inlet flue gas temperature, etc., and This application is for coal-fired units, which have large detection data values ​​and large prediction errors, which have a great impact on subsequent control

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