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 in power plants, and further judge whether they affect key performance indicators (KPIs), such as the thermal efficiency. These two goals can be achieved by KPI-related nonstationary process monitoring. Although the thermal efficiency cannot be accurately measured online, it can be strongly characterized by some online measurable variables, including the exhaust gas temperature and oxygen content of flue gas. These critical variables closely related to the thermal efficiency are termed as output variables. Inspired from nonstationary common trends between input and output variables in thermal power plants, the output-relevant common trend analysis (OCTA) method is proposed, in this article, to model the input-output relationship. In OCTA, input and output variables are decomposed into nonstationary common trends and stationary residuals, and the model parameters are estimated by solving an optimization problem. It is pointed out that OCTA is a generalized form of partial least squares (PLS). The superior monitoring performance of OCTA is illustrated by case studies on a real power plant in Zhejiang Provincial Energy Group of China. Compared with the other PLS-based recursive algorithms, OCTA can effectively detect the anomalies in power plants and accurately determine whether they have an impact on the thermal efficiency or not.

Automated summary

This article discusses the importance of operation safety and efficiency in power plants, and introduces the concept of KPI-related nonstationary process monitoring to detect anomalies and assess their impact. The Output-relevant Common Trend Analysis (OCTA) method is proposed to model the relationship between input and output variables in thermal power plants, showing superior monitoring performance in detecting anomalies and determining their impact on thermal efficiency.