基于EWT-LOF的热工过程数据异常值检测方法

Outlier detection method for thermal process data based on EWT-LOF

异常数据检测是热工过程数据处理的重要组成部分,也是进行系统建模、优化、控制的基础。针对热工过程频繁变工况导致异常数据检测困难的情况,提出一种将信号分解方法与基于密度的检测方法相结合的热工过程异常值检测方法。首先利用经验小波变换方法提取热工过程时间序列的运行趋势,去除序列运行趋势后采用局部离群因子方法对各数据点求取其局部异常值,最后使用箱型图的方法确定序列异常点。通过使用某电厂1 000 MW机组的负荷数据作为实验数据,分别设置0.5%、1%、2%、5%、10%5种误差验证方法的有效性。实验结果表明,所提异常检测方法除对动态过程和稳态过程均具有适用性外,在以上5种误差条件下均取得了较高的检测准确率。

Outlier detection is an important part of data processing in thermal process, and is also the basis for system modeling, optimization and control. Aiming at the problem that the operational condition of the thermal process changes frequently, which causes the difficulty of outlier detection, this paper proposes a thermal process outlier detection method combining signal decomposition method and density-based detection method. Firstly, the empirical wavelet transform method is used to extract the operational trend of the thermal process time series. After removing the sequence operational trend, the local outlier factor method is used to obtain the local outlier values for the data points. Finally, the box plot method is used to determine the sequence outlier points. The load data of the 1000 MW unit in a certain power plant was used as the experiment data, five errors of 0.5%, 1%, 2%, 5% and 10% were set respectively to verify the effectiveness of the method. The experiment results show that besides having applicability to both dynamic and steady state processes, the outlier detection method proposed in this paper achieves high detection accuracy under the above five error conditions.