基于状态识别的经验模态分解法火电厂运行数据预处理

Operating data preprocessing using EMD method with state recognition for thermal power plants

针对火电机组运行监测数据量大且复杂情况下的去噪问题,提出一种基于状态识别的改进经验模态分解去噪(SREMD)算法。该算法以经验模态分解(EMD)方法为基础,首先运用基于拉伊达(Pauta)准则的滤波方法去除显著异常数据,然后根据数据连续变化率动态识别机组运行状态,最后根据机组运行状态的稳态过程和过渡过程分别进行针对性EMD去噪,以适应火电机组的状态切换特性。将该算法用于实际机组运行数据,结果表明,本文算法有效完成了火电机组监测数据的去噪预处理,在保持信号整体趋势的基础上能达到更好的去噪效果。

The data from the operating thermal power units are very large and contains complex noise,which is hard to eliminate the noise thoroughly.To solve this problem,an improved empirical mode decomposition filtering algorithm based on the state recognition(SREMD)was proposed.On the basis of empirical mode decomposition(EMD),this SREMD algorithm firstly eliminates the significantly abnormal data by using a filtering method which is based on the Pauta criterion.Then,it recognizes the running states of the unit according to continuous change rate of the data.Finally,it performs corresponding EMD denoising according to the steady state and transition process of unit operation,to adapt to the state switching characteristics.Moreover,this algorithm was applied for actual unit operation data.The results show that,the proposed SREMD algorithm effectively completed the pre-processing of the monitoring data of thermal power units and achieved a better denoising effect on the basis of maintaining the overall trend of the signal.