Abstract:
For a steam power plant, condenser vacuum is an important variable to monitor. Insufficient condenser vacuum can cause high heat rate. An approach is proposed to modeling condenser vacuum based on the autoregressive-moving-average (ARMA) combined with the generalized-autoregressive-conditional-heteroscedasticity (GARCH) technique. It makes use of data available from a generating unit of the Asam-asam Steam Power Plant over a period when the unit was running under severe off-design conditions. In that period, the unit experienced poor condenser vacuum. The data contain observations on variables, some of which are important for studying conditions regarding condenser vacuum at the unit. The resulting models can explain how condenser vacuum varies in response to changes in conditions. The predictive performance is comparable to that obtained using autoregressive neural network and support vector regression. Further remarks on the modeling issues are given.
