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基于机器学习的火力发电机组运行在线监测研究

Research on Online Monitoring of Thermal Power Unit Operation Based on Machine Learning

  • 摘要: 为实现对机组运行状态的实时监测,提高其运行的可靠性与稳定性,基于机器学习算法设计火力发电机组运行在线监测方法。利用稳态方程和动态方程建立火力发电机组运行系统模型,并利用机器学习中的循环神经网络进行火力发电机组运行工况预测;搭建人机交互界面,进行机组运行中关键参数实时在线监测。对比实验结果表明,应用设计的方法后,可实现对火力发电机组运行效率的在线监测,监测结果与人工监测结果(期望输出结果)一致性较高,证明其可靠性较强。

     

    Abstract: In order to achieve real-time monitoring of the operating status of the unit and improve its reliability and stability, this paper will take the thermal power generation unit as an example and design an online monitoring method based on machine learning algorithms during its operation. Establish a model of the operating system of thermal power generation units using steady-state equations and dynamic equations, and use recurrent neural networks in machine learning to predict the operating conditions of thermal power generation units; build a human-computer interaction interface for real-time online monitoring of key parameters during unit operation. The comparative experimental results show that after applying the designed method, online monitoring of the operating efficiency of thermal power generation units can be achieved. The consistency between the monitoring results and the manual monitoring results(expected output results) is high, indicating its strong reliability.

     

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