Output-Relevant Common Trend Analysis for KPI-Related Nonstationary Process Monitoring With Applications to Thermal Power Plants

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 10; pp. 6664 - 6675
• Main Authors: Wu, Dehao, Zhou, Donghua, Chen, Maoyin, Zhu, Jifeng, Yan, Fei, Zheng, Shuiming, Guo, Entao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Anomaly detection; Coal; common trend analysis; Efficiency; Exhaust gases; fault diagnosis; Flue gas; Gas temperature; Informatics; key performance indicator (KPI); Monitoring; nonstationary process monitoring; Optimization; Oxygen content; Parameter estimation; Power generation; Power measurement; power plant; Power plants; Process monitoring; Temperature measurement; thermal efficiency; Thermal power plants; Thermodynamic efficiency; Thermoelectricity; Trend analysis; Trends
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3041516

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.