Autocorrelation Feature Analysis for Dynamic Process Monitoring of Thermal Power Plants

Accurate process monitoring plays a crucial role in thermal power plants since it constitutes large-scale industrial equipment and its production safety is of great significance. Therefore, accurate process monitoring is very important for thermal power plants. The vigorous nature of the production process requires dynamic algorithms for monitoring. Since the common dynamic algorithm is mainly based on data expansion, the online computing complexity is too high because of data redundancy. Accordingly, this article proposes an innovative, dynamic process monitoring algorithm called autocorrelation feature analysis (AFA). AFA mines the dynamic information of continuous samples by calculating the correlation between the current time and past time features. While improving the monitoring effect, the AFA algorithm also has extremely low online computational complexity, even lower than common static algorithms, such as principal component analysis. Furthermore, this study exhibits the general form of dynamic additive faults for the first time and verifies the reliability of the algorithm through fault detectability analysis. Conclusively, the superiority of the AFA algorithm is verified on a numerical example, continuous stirred tank reactor (CSTR), and real data measured from a 1000-MW ultrasupercritical thermal power plant.

Automated summary

This article proposes an innovative dynamic process monitoring algorithm called autocorrelation feature analysis (AFA) which mines the dynamic information of continuous samples by calculating the correlation between current and past time features. The AFA algorithm has extremely low online computational complexity and has been verified on a CSTR and real data from a 1000-MW ultrasupercritical thermal power plant, showing its superiority.