Development of a three-term MPC and its application to an ultra-supercritical coal fired power plant

Most MPC (Model Predictive Control) algorithms used in industries and studied in the control academia use a two-term QP (quadratic programming), where the first term is the weighted norm of the output errors, and the second term is that of the input increments. In this work, a DMC (Dynamic Matrix Control) algorithm that uses three-term QP is developed and studied, where the third term is the weighted norm of the output increments. A relationship between the three-term DMC and the two-term DMC is established; and based on that, ideal closed-loop response curves are derived. These results are useful for the tuning of the three-term DMC. Then, a method for comparing two control methods is proposed and it is shown that the three-term DMC outperforms the two-term DMC. The findings are verified using simulation studies. Finally, the three-term DMC is successfully applied to a 1030 MW ultra-supercritical coal-fired power plant. According to simulation study using the identified model, in comparison to traditional two-term DMC, there is a roughly 10% reduction in the standard deviation of load tracking error for similar control efforts. In real-life tests on the power plant, the performance of the three-term DMC is compared to that of the existing PID controller. The load rate of change increased from 0.8%/min to 1.5%/min while the standard deviation of steam pressure decreased 38%; the standard deviations of the main steam temperature and reheat steam temperature decreased more than 40% with their mean values increased by more than 2 degrees C and 4 degrees C respectively.

Automated summary

In this study, a three-term Dynamic Matrix Control (DMC) algorithm using quadratic programming is developed and compared with the traditional two-term DMC algorithm. Simulation studies and real-life tests show that the three-term DMC algorithm outperforms the two-term DMC algorithm in control effectiveness.