基于ILSO-DELM的燃气轮机压气机故障预警方法  

Fault Warning Method for Gas Turbine Compressor Based on ILSO-DELM

作　　者：马梦甜 茅大钧 蒋欢春 MA Meng-tian;MAO Da-jun;JIANG Huan-chun(College of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Shanghai Minghua Electric Power Technology Co.,Ltd,Shanghai 200090,China)

机构地区：[1]上海电力大学自动化工程学院,上海200090 [2]上海明华电力科技有限公司,上海200090

出　　处：《电工电气》2024年第5期63-68,共6页Electrotechnics Electric

基　　金：上海市“科技创新行动计划”地方院校能力建设专项项目(19020500700)。

摘　　要：压气机结构复杂,运行特性为非线性的特点加大了燃气轮机压气机故障预警的难度,为了提高燃气轮机压气机故障预警能力,提出了一种基于改进的狮群优化算法(ILSO)优化深度极限学习机(DELM)的故障预警方法。通过皮尔逊相关分析得到与预警参数相关性高的测点,构建ILSO-DELM预测模型,得到正常状态下预警参数的绝对值,通过参数估计确定阈值,根据残差绝对值是否超过预警线来间接判断压气机的运行情况。以上海某燃机电厂的运行数据进行分析,通过验证表明:该方法能够对压气机故障提前预警,并且相比于DELM模型预测精度更高。The complexity of the compressor structure and the nonlinear characteristics of its operation pose challenges in predicting faults in gas turbine compressors.To enhance the fault prediction capability of gas turbine compressor,a novel approach is proposed using an improved lion swarm optimization(ILSO)to optimize deep extreme learning machine(DELM)for fault prediction.Through Pearson cor-relation analysis,the measurement points with high correlation with the early warning parameters are obtained,the ILSO-DELM prediction model is constructed,the absolute value of the early warning parameters under normal conditions is obtained,the threshold is determined by parameter estimation,and the operation of the compressor is indirectly judged according to whether the absolute value of the residual exceeds the early warning line.Based on the analysis of the operation data of a gas turbine power plant in Shanghai,the verification shows that the proposed method can give early warning of compressor faults,and the prediction accuracy is higher than that of the DELM model.

关 键 词：压气机 深度极限学习机 狮群优化算法 故障预警 

分 类 号：TK478[动力工程及工程热物理—动力机械及工程]