基于稳健状态估计的设备状态预警研究

Early warning of equipment state based on robust state estimation

针对发电机组设备的状态监测问题,采用改进的自联想回归(AAKR)算法建立设备的状态预警模型。将稳健距离算子代替马氏距离算子,提高自回归模型的稳健性和抗污染能力。将聚类思想应用于距离划分,提出一种基于聚类的变间隔状态矩阵提取方法,并给出具体的实施步骤。采用四重交叉验证学习机制,在训练过程中对模型参数进行优化,获得最优的状态监测模型。以某600 MW机组一次风机为例建立状态参数估计模型,DCS实际运行数据和仿真计算结果表明,改进后的稳健状态估计方法在维持较高准确性的前提下,能够大幅提高模型的稳健性,具有较强的抗污染能力。这样在设备故障早期就能给出相关预警信号,为设备的状态检修提供理论依据。

According to the state monitoring problem of power plant equipment,an improved auto-association kernel regression method is applied to establish the state early warning model for power plant equipments.The Mahalanobis distance operator is replaced with robust distance operator to improve the robustness and anti-pollution ability of the autoregressive model.By applying clustering ideas to distance partitioning,a variable interval state matrix extraction method based on clustering is proposed,and the specific implementation steps are also given.During training period,the model parameters are optimized by using quadruple cross-validation learning mechanism,and an optimal state monitoring model is obtained.Moreover,by using the primary air fan in a 600 MW unit as the example,the state parameter estimation model is established.The actual operational data in DCS and the simulation result shows that,the improved robust state estimation method can greatly improve the robustness of the model while maintaining high accuracy,which has strong anti-pollution ability.Thus,in the early stages of the failure,it is able to give early warning signals in time.This method provides a theoretical basis for state maintenance for power plants.