Visual Analytics of Time Series Data of Thermal Power Control for System Identification

The analysis and modeling of historical data of thermal power control can help users achieve better operation control. Due to the high data complexity, traditional system identification approaches are very complicated, and are difficult to obtain effective results. This paper integrates visual analytics into the process of system identification, called imDCS. The system supports the entire process of control system modeling in all stages including time series feature analysis, model establishment, selection and evaluation, and model iteration optimization. By using various visual mappings and coordinated views, it allows for multi-level model selection, and illustrates high-dimensional and multivariate model structure. By using accuracy evaluation combined views, it supports model performance evaluation from different perspectives. We worked with domain experts to case studies and evaluation based on real control data and operation optimization requirements of a power plant. The results verify the effectiveness and usability of our approach in industrial control data analysis and modeling.