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The variable operating condition of thermal power units makes the data show multi-modal characteristics, which leads to the decrease of prediction accuracy of the regression soft sensor model based on shallow network structure. An improved BP neural network (back propagation neural network, BPNN) soft sensor method is studied. Firstly, the original data features are extracted by using the strong deep learning ability of stacked sparse autoencoder (SSAE), and then the extracted features are analyzed by BPNN. The experimental results show that, the mean square error of the SSAE+BPNN soft sensor method is 0.135 8×10^–3 and the square correlation coefficient is 0.983 2. It is proved that its prediction accuracy and generalization ability are significantly better than those of BPNN. It is applied to the soft sensor of carbon content in fly ash of a flexible peak-shaving 660 MW ultra-supercritical generator set, and the average relative error of the prediction results is 0.91%, the overall relative error is less than±5%, indicating the method has good engineering application value.

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火电机组变工况运行使数据呈现多模态特征，导致基于浅层网络结构的回归软测量模型的预测精度下降。研究一种改进的BP神经网络（back propagation neural network, BPNN）软测量方法：首先利用堆叠稀疏自编码器（stacked sparse autoencoder, SSAE）强大的深度学习能力提取原始数据特征，然后再利用BPNN对提取特征进行回归分析。经实验验证，SSAE+BPNN软测量方法的均方误差为0.135 8×10^–3，平方相关系数为0.983 2，其预测精度和泛化能力显著优于 BPNN。将其应用于某台灵活调峰的超超临界660 MW发电机组飞灰含碳量软测量中，预测结果的平均相对误差为0.91%，总体相对误差控制在±5%以内，具有良好的工程应用价值。

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刘鑫屏（1976），女，博士，副教授，主要研究方向为大机组智能优化控制、热力发电过程建模与状态参数检测、综合能源系统，lxp@ncepu.edu.cn。

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刘鑫屏（1976），女，博士，副教授，主要研究方向为大机组智能优化控制、热力发电过程建模与状态参数检测、综合能源系统，lxp@ncepu.edu.cn。

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