Guodian Environmental Protection Research Institute Co., Ltd

煤电脱硫脱硝的正常运行对电力系统的安全稳定有着重要影响,但是传统预测方法存在准确率不高的问题,因此提出一种改进PSO-Attention-LSTM的煤电脱硫脱硝运行状态预测方法。首先,建立优化煤电脱硫脱硝运行状态的主要指标及其权重指标,在数据输入阶段,通过PSO-Attention-LSTM获取运行状态数据相关的时空特征,对煤电脱硫脱硝运行状态作出预测,完成煤电脱硫脱硝潜在性故障的预警信息。试验结果显示,该预测方法对煤电脱硫脱硝运行状态的预测精度在84%,能够较好准确预测煤电脱硫脱硝的运行状态,可用于煤电脱硫脱硝运维管理的参考辅助。

The normal operation of coal-fired desulfurization and denitrification has a significant impact on the safety and stability of the power system. However, traditional prediction methods have the problem of low accuracy. An improved PSO-Attention LSTM method for predicting the operational status of coal-fired desulfurization and denitrification is proposed. Firstly, establish the main indicators and their weight indicators for optimizing the operation status of coal-fired power desulfurization and denitrification. In the data input stage, obtain the spatiotemporal characteristics related to the operation status data through PSO-Attention LSTM, predict the operation status of coal-fired power desulfurization and denitrification, and complete the warning information of potential faults in coal-fired power desulfurization and denitrification. The experimental results show that the prediction accuracy of this prediction method for the operational status of coal-fired power desulfurization and denitrification is 84%, which can effectively and accurately predict the operational status of coal-fired power desulfurization and denitrification. It can be used as a reference assistance for the operation and maintenance management of coal-fired power desulfurization and denitrification.

侯深, 祝业青, 李祥, 潘云. 基于PSO-Attention-LSTM算法的煤电脱硫脱硝运行状态预测方法. 煤炭经济研究. 2024, 44(8): 60-64

HOU Shen, ZHU Yeqing, LI Xiang, PAN Yun. Method forpredicting the operation status of coal electricity desulfurization and denitration based on PSO-Attention-LSTM algorithm. Coal Economic Research. 2024, 44(8): 60-64