Modeling and Predicting Steam Power Plant Condenser Vacuum based on Small-sized Operation Data

Abstract

The condenser vacuum is an important variable in steam power plants. Monitoring and controlling this variable requires predicting its behavior. This paper develops further Autoregressive-Generalized Autoregressive Conditional Heteroscedasticity (AR-GARCH) models for this purpose, using lagged values of predictors. The predictors include the inlet temperature of the condenser cooling water and the active power of the generator. Models can be adequately trained with small-sized data, making them suitable for use in thermal plants, which are often regularly maintained with operating conditions being reset, rendering past data obsolete. Training and testing were carried out using operation data from an actual steam power plant generating unit during a period in which it faced the prospect of an emergency turbine shutdown. When the models pass all the required statistical tests, they tend to outperform other techniques, including autoregressive neural networks and support vector regression, in terms of prediction. This study also discusses an implementation scenario. The choice of training sizes and model variants can be flexible, enhancing the models' practicality for real operational situations. This study also provides additional directions for further research.