关键词:

随机系统, 过程监控, 主元素分析, 电厂

Abstract:

The monitoring and fault diagnosis of large-scale power plant processes that exhibit non-stationary and/or time-varying behavior were discussed. The work considered statistically-based monitoring technique, which was related to the multivariate statistical process control (MSPC) framework. Particular focus is on principal component analysis (PCA)，as this technique allows distinguishing between cause and effect variables consequently. To demonstrate the difficulties of monitoring processes with non-stationary and time-varying behavior, the use of conventional PCA was compared with its recursive and fast moving-window counterparts. A recently proposed recursive moving window technique was employed because of its ability in adapting to process changes and its computational efficiency. The advance in fault detection was demonstrated by comparing fast moving-window PCA (MWPCA) with the conventional PCA. In addition, this paper proposes to plot the scaled variables in conjunction with fast MWPCA for fault diagnosis, which proves to be effective in power utility process application.

Key words:

随机系统, 过程监控, 主元素分析, 电厂