基于GRA-DeepAR的燃气轮机燃烧室故障预警研究

为防止电站燃气轮机燃烧室在工作期间出现故障而导致重大经济损失,提出了一种基于灰色关联度分析和深度自回归模型(GRA-DeepAR)的故障预警方法。首先,采集燃气轮机的正常运行数据进行GRA,提取出与透平排气温度高度相关的特征参数;然后,采用DeepAR对透平排气温度进行预测,并设定预警阈值,以此建立深度自回归故障预警模型;最后,根据残差绝对值是否超过预警线来间接判断燃烧室的运行情况。以某电厂安萨尔多燃气轮机的运行数据为例进行了分析,结果表明:该方法能够提前识别燃烧室的异常状况,同时发出预警信号提醒工作人员进行处理,可为燃气轮机燃烧室故障预警提供实际参考。

Research on Gas Turbine Combustion Chamber Fault Warning Based on GRA-DeepAR

In order to prevent the major economic losses caused by the failure of the gas turbine combustion chamber in the power plant during the working period,a fault early warning method based on Grey Relation Analysis-Deep Autoregressive Model (GRA-DeepAR) is proposed.Firstly,the normal operation data of the gas turbine are collected for GRA,and the characteristic parameters highly related to the turbine exhaust temperature are extracted; Then,DeepAR is used to predict the turbine exhaust temperature,and an early warning threshold is set to establish a deep autoregressive fault early warning model; Finally,the operation of the combustion chamber is judged indirectly according to whether the absolute value of the residual exceeds the warning line.Taking the operation data of Ansaldo gas turbine in a power plant as an example,the results show that the method can identify the abnormal conditions of the combustion chamber in advance,and send an early warning signal to remind the staff to deal with them,providing practical reference for the early warning of gas turbine combustion chamber failure.