电子测量与仪器学报

韩天翔,史元浩,李孟威,梁建宇,杨彦茹,王江.融合 CEEMD 和 TCN 的受热面积灰预测研究[J].电子测量与仪器学报,2022,36(10):108-114

融合 CEEMD 和 TCN 的受热面积灰预测研究

Study on the prediction of heated area ash fused with CEEMD and TCN

DOI：

英文关键词:cleaning factor boiler ash deposit complementary set empirical modal decomposition time convolutional network

基金项目:国家自然科学基金（72071183）、山西省自然科学基金(201901D111164)、山西省回国留学人员科研资助项目(2020 114)、中国高校产学研创新基金项目(2019ITA0cxy0023)资助

作者	单位
韩天翔	1.中北大学电气与控制工程学院
史元浩	1.中北大学电气与控制工程学院
李孟威	1.中北大学电气与控制工程学院
梁建宇	1.中北大学电气与控制工程学院
杨彦茹	1.中北大学电气与控制工程学院
王江	1.中北大学电气与控制工程学院

Author	Institution
Han Tianxiang	1.School of Electrical and Control Engineering, North University of China
Shi Yuanhao	1.School of Electrical and Control Engineering, North University of China
Li Mengwei	1.School of Electrical and Control Engineering, North University of China
Liang Jianyu	1.School of Electrical and Control Engineering, North University of China
Yang Yanru	1.School of Electrical and Control Engineering, North University of China
Wang Jiang	1.School of Electrical and Control Engineering, North University of China

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中文摘要:

对锅炉受热面积灰程度的有效预测,可为锅炉提升生产效率和故障预警提供重要依据。采用清洁因子来评估受热面的灰污沉积状况,针对其序列非线性、非平稳性的特点,提出一种基于互补集合经验模态分解与时间卷积网络的受热面积灰预测方法。首先,通过互补集合经验模态分解将经过小波阈值去噪处理后的原始序列分解为一组子序列分量;然后,针对不同子序列分别构建基于时间卷积网络的时序预测模型,并优化网络超参数提升预测准确性;最后,将各 IMF 分量的预测结果叠加得出清洁因子的预测数值。由实验结果可得,相较于其他两种模型,预测精度分别提高 62. 1%和 57. 1%,CEEMD-TCN 模型对受热面积灰状况预测精度最高,验证了该模型的准确性和可靠性。

英文摘要:

Effective prediction of the degree of ash in the heated area of the boiler can provide an important basis for boiler production efficiency and fault early warning. The cleaning factor is used to evaluate the ash deposition status of the heated surface, and according to the characteristics of nonlinearity and non-stationariness of the sequence, a method of predicting the heated area ash based on the empirical modal decomposition and time convolutional network of complementary sets is proposed. Firstly, the original sequence after wavelet threshold denoising is decomposed into a set of sub-sequence components by complementary set empirical mode decomposition, then the time series prediction model based on the time convolutional network is constructed for different sub-sequences, and the network hyperparameters are optimized to improve the prediction accuracy; finally, the prediction results of each IMF component are superimposed to obtain the prediction values of the cleaning factor. Compared with the other two models, the prediction accuracy is improved by 62. 1% and 57. 1%, respectively, and the CEEMD-TCN model has the highest prediction accuracy for the ash condition of the heated area, which verifies the accuracy and reliability of the model.

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