Research On Thermal Process Modeling And Diagnosis Methods Based On Deep Learning

Flexible peaking operation of large thermal power units can be complementary to intermittent renewable energy,which will be an important effort direction for the electric power industry in our country to realize sustainable development and the goal of "carbon reaching peak,carbon neutrality".Intelligent monitoring of thermal power units is an effective means to ensure the long-term flexible operation of large units.Real-time monitoring of equipment operating status,timely detection of abnormalities,identification of faults,and rapid formulation of fault recovery measures have important theoretical significance and engineering application value for the safety and economy of the entire energy system.This paper focuses on the research about thermal process modeling and diagnosis methods based on deep learning.The main research contents are as follows:(1)Aiming at the data imbalance problem in the nonlinear modeling process of power plant equipment,a data enhancement method based on CVAE-GAN model is proposed.The CVAE-GAN generation model based on VAE and WGAN-GP is established,and the continuous condition is introduced to learn the original data distribution.To improve the quality of generated data and generating efficiency,the enhancement method based on CVAE-GAN model is proposed.The incremental L2-discrepancy can guide the generation training procedure to obtain high-quality generated samples and guide to remove redundant data when the dataset is too large.A numerical simulation case is given to validate the superiority of the proposed model over other common generative models.Then,the proposed model is applied for NOx emission prediction in a coal-fired power plant.(2)Aiming at the multi-modal and variable-condition operation characteristics of power station equipment,a multi-modal hybrid modeling method incorporating physical constraints is proposed.By building an LSTM-based data-driven model in a single modality and introducing constraints based on conservation of physics in the loss function of the model,a dynamic hybrid model incorporating physical constraints in a single modality is constructed.Aiming at more complex multi-modal modeling problems,two multi-modal switching strategies are constructed through the attention mechanism,and the trained single-modal hybrid models are integrated to establish a multi-modal hybrid modeling framework to learn the multimodal operation characteristics of power plant equipment.Taking the temperature monitoring problem of superheated steam in a superheated system as an example,the effectiveness of the proposed modeling method is verified.(3)Aiming at the correlation between the abnormal state of power plant equipment and process variables and target variables,an online fault detection method for nonlinear systems based on IBVAE-SR model is proposed.Considering the extensibility of the hidden layer structure of β-VAE network,the IBVAE-SR model integrating the deep variational information bottleneck theory is proposed.To improve the efficiency of model training,the equilibrium coefficient β in the model loss function is fixed and the threshold C to be trained is introduced to ensure the reconstruction accuracy and feature composition ability of the model.To improve the effect of fault detection,the feature statistics and residual statistics corresponding to hidden layer variables and model residuals are designed to detect and distinguish faults related to or unrelated to the target variables.Taking mathematical simulation as a verification example and coal mill system as an application example,the validity of the fault detection method based on the IBVAE-SR model proposed in this paper is verified.(4)Aiming at the spatiotemporal characteristics of thermal power units,a fault diagnosis method for nonlinear dynamic systems based on DIBVAE-SR model is proposed.Based on the IBVAE-SR model proposed in Chapter 4,the dynamic model of DIBVAE-SR is established by introducing the Seq2 seq network structure based on Bi-GRU,and the fault detection framework based on this model is constructed.To achieve fault isolation,a spatial self-attention network layer is introduced in the model,and the process variables related to faults are judged by observing the changes of self-attention weights.By using the t-SNE method and the DBSCAN density clustering method,feature visualization and fault classification are performed on the hidden layer variables of the model.Taking the CSTH process as the verification example and the cold end system as the application example,the effectiveness of the fault diagnosis method based on the DIBVAE-SR model proposed in this paper is verified.(5)A thermal process modeling and fault diagnosis system that can be applied in the field operation process of thermal power units is designed and developed.The system construction scheme is given from the aspects of system architecture,functional interaction and field deployment,and a field development case based on the SIS database of thermal power units is given.