改进型灰色关联度分析电厂涡轮机油的监测数据

Analysis of monitoring data of turbine oil in power plant by improved grey relational degree

针对电厂涡轮机油在运行过程中监测数据存在离散程度较大,难以预测且预测精确度低等问题,建立了最优权值灰色关联评价体系模型,以涡轮机油的铁含量作为系统行为特征序列,定量分析运动粘度、酸值、水分与铁含量的关联性。实例分析结果表明,通过优化灰色GM(1,1)模型评价涡轮机油的运行状态具有较高的预测精度,预测结果与实际测量值基本吻合,为涡轮机油运行状态的评价提供了依据。

Aimed at the problems that the monitoring data of turbine oil performance exist large discrete degree,the difficult to predict and low in prediction accuracy,the optimal weight gray correlation evaluation system model was established.The iron content of turbine oil was taken as the sequence of system behavior characteristics.The relativity of kinematic viscosity,acid value,moisture and iron content was quantitatively analyzed.The example analysis results show that the optimization of the grey GM(1,1)model to evaluate the operating state of turbine oil has higher prediction accuracy,and the prediction results are basically consistent with the actual measured values,which provides a basis for the evaluation of turbine oil operating state.