火电厂设备运行参数监测预警研究与应用

Research and application on monitoring and early warning of equipment operation parameters in thermal power plant

火电是我国发电行业的主力,是国家经济发展的重要支柱。实际生产中,火力发电过程工艺机理复杂,工况较多,各个设备运行参数之间相关性强,火电机组设备异常频发[1],基于神经网络LSTM模型使用设备在正常工况下的运行参数建立模型告警模型,模型识别设备运行时发生的异常,尽早发现设备的劣化趋势,提前给出预警信号。本文针对火电机组设备运行参数异常,提出了基于机器学习设备运行参数相关性分析运行参数监测预警方法,并将所提出的方法成功应用于实际火力发电过程中。

Thermal power is the main force of China's power generation industry and an important pillar of national economic development.In actual production,thermal power generation process has complex technological mechanism,many working conditions,strong correlation between the operating parameters of various equipment,and frequent anomalies of thermal power units[1].Based on the neural network LSTM model,the operating parameters of equipment under normal working conditions are used to establish a model alarm model,which can identify the anomalies occurring during equipment operation and detect the deterioration trend of equipment as soon as possible.Give early warning signs.In this paper,a monitoring and early warning method based on the correlation analysis of equipment operating parameters of thermal power units is proposed,and the proposed method is successfully applied to the actual thermal power generation process.