Coal-fired unit water wall temperature prediction neural network model

Abstract

The invention discloses a coal-fired unit water wall temperature prediction neural network model, and the model is characterized in that the model is formed by successive connection of a plurality ofneural networks, and each neural network is composed of an input layer, a hidden layer and an output layer; the input layer is divided into a predicted heating surface wall temperature variable input,an upstream heating surface wall temperature variable input and other key variable inputs, key factors influencing the wall temperature and the upstream heating surface wall temperature change condition are considered, and meanwhile the influence of predicted heating surface wall temperature historical data on the input layer is considered; an input variable structure is determined, an input parameter delay coefficient, the number of hidden layers and an activation function are corrected, the model training and generalization precision is improved, the change trend of the predicted wall temperature at different moments is obtained through successive wall temperature prediction, and the better water wall temperature prediction precision is achieved.

Description

technical field [0001] The invention relates to the technical field of modeling the wall temperature characteristics of a heating surface of a thermal power unit, in particular to a neural network model for predicting the wall temperature of a water-cooled wall of a coal-fired unit. Background technique [0002] Facing the increasingly serious pressure of environmental protection, the country continues to promote the adjustment of energy structure, and clean energy such as wind energy and solar energy has achieved sustained and rapid development. However, due to the general randomness and intermittent characteristics of wind energy, solar energy and other new energy sources, large-scale grid connection will inevitably have a certain impact on the security and stability of the grid; Adjustment, the contradiction between my country's overall power supply and demand has changed from a shortage to a relative surplus. Therefore, in order to improve the absorption capacity of wind...

AI Technical Summary

Problems solved by technology

Among them, the wall temperature is calculated through the mechanism modeling analysis of water-cooled walls, superheaters and other components. This method is more complicated, and there are many boundary parameters. The actual measurement points of the power plant cannot give all the boundary parameters, and the model needs to be continuously revised under different conditions. Therefore, It does not meet the requirements of online calculation, and cannot participate in the closed-loop control of the wall temperature of the power station in real time; based on the mathematical modeling analysis method, the wall temperature prediction method based on the artificial neural network is mostly used at present, only considering the influence of external factors on the wall temperature, using BP Prediction of Boiler Tube Wall Temperature Using Static Network Structures such as Neural Network

The historical data of the current wall temperature, the historical data of the upstream wall temperature, and the rate of change of related factors have not been considered, and the time series prediction neural network structure, neural network activation function, etc. have not been studied. The prediction structure is relatively backward and the calculation results are poor.

[0008] To sum up, the existing countermeasures and prediction methods for wall temperature overtemperature only stop at displaying alarms, so that parameters can be changed with the experience of operating personnel, and closed-loop control has not been realized.

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