Output-relevant common trend analysis for KPI-related nonstationary process monitoring with applications to thermal power plants

Operation safety and efficiency are two main concerns in power plants. It is important to detect the anomalies, and further judge whether they affect key performance indicators (KPIs), such as the thermal efficiency. The two goals can be achieved by KPI-related nonstationary process monitoring. Inspired from nonstationary common trends between input and output variables in power plants, the output-relevant common trend analysis (OCTA) method is proposed to model the input-output relationship. In OCTA, the variables are decomposed into nonstationary common trends and stationary residuals, and model parameters are estimated by solving an optimization problem. It is revealed that OCTA is a generalized form of partial least squares (PLS). The superior performance of OCTA is illustrated by case studies on a real power plant. Compared with other PLS-based recursive algorithms, OCTA can effectively detect the anomalies and accurately determine whether they have an impact on the thermal efficiency or not.