Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network

doi:10.12096/j.2096-4528.pgt.21084

Abstract

Abstract:

In order to obtain the online monitoring information of boiler furnace temperature field in thermal power plant quickly and accurately, a temperature field reconstruction algorithm of acoustic tomography (AT) based on deep neural network (DNN) was proposed. After normalizing the measured values, combined with principal component analysis (PCA) dimension reduction, a fully connected network was constructed to distinguish the peak type. Moreover, DNN and BP neural network were built to predict the normalized slowness value and its maximum value, respectively. Finally, the temperature field distribution was reconstructed. Four typical temperature field models were simulated by using this method. The results show that the reconstruction quality of DNN algorithm is better than that of Tikhonov regularization algorithm and conjugate gradient algorithm. In addition, the average relative error and root mean square error of reconstructed image are less than 0.36% and 0.85% respectively.

Key words: thermal power plant, power plant boiler, temperature field, acoustic tomography (AT), deep neural network (DNN), principal component analysis (PCA)

CLC Number:

TK 31

Lifeng ZHANG, Jing LI, Zhi WANG. Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network[J]. Power Generation Technology, 2023, 44(3): 399-406.

Figures/Tables 15

Fig. 1 Flow chart of temperature field reconstruction

Fig. 2 Structure diagram of fully connected network

Fig. 3 Structure diagram of DNN

Fig. 4 Structure diagram of BP neural network

Fig. 5 Schematic diagram of grid division

Tab. 1 Influence of PCA on DNN network training times

峰型	训练次数
峰型	PCA处理后	PCA处理前
单峰	60	100
双峰	140	200
三峰	60	140
四峰	100	200

Fig. 6 Reconstruction results of single peaktemperature field

Tab. 2 Reconstruction error of single peaktemperature field

重建算法	平均相对误差/%	均方根误差/%
DNN	0.33	0.47
Tikhonov	0.70	1.50
共轭梯度	0.72	1.80

Fig. 7 Reconstruction results of twin-peaktemperature field

Tab. 3 Reconstruction error of twin-peaktemperature field

重建算法	平均相对误差/%	均方根误差/%
DNN	0.36	0.85
Tikhonov	0.62	0.97
共轭梯度	0.59	1.04

Fig. 8 Reconstruction results of three-peaktemperature field

Tab. 4 Reconstruction error of three-peaktemperature field

重建算法	平均相对误差/%	均方根误差/%
DNN	0.31	0.67
Tikhonov	0.62	0.94
共轭梯度	0.51	0.86

Fig. 9 Reconstruction results of four-peaktemperature field

Tab. 5 Reconstruction error of four-peaktemperature field

重建算法	平均相对误差/%	均方根误差/%
DNN	0.36	0.62
Tikhonov	0.64	0.98
共轭梯度	0.50	0.89

Fig. 10 Average relative error of temperature distribution reconstruction when the noise level is 0.5%

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