Application of Digital Twin Combined LSSVM Algorithm in Control Loop State Monitoring

Abstract

The high-pressure water level control system is essential for controlling the economic benefits of power plants. The study aimed to explore the utilization of digital twin technology combined with least squares support vector machine (LSSVM) in control loop state monitoring. By constructing digital twin models and applying LSSVM, we aimed to achieve high-precision monitoring of the control system states and facilitate effective prediction of abnormal situations. During model training and error testing, the outcomes demonstrated that the model could accurately match the sample data, and the output error of the model was primarily concentrated in the [-0.1, 0.1] interval. In the case of changes in system characteristics, the model effectively adapted and maintained a low mean square error via the introduction of an online update strategy. The model exhibited a good fit and prediction accuracy for the specific monitoring of the water level control circuit of a high pressure heater. The effectiveness of the model was further validated through measurements of the water level process at different time periods. Thus, this study demonstrated the effectiveness and feasibility of combining digital twins and LSSVM in control system monitoring. The proposed method facilitated cost reduction and efficiency increase in power plants, thereby promoting their production and development level.