Model-based characterization of pressure/load relationship for power plant load control

Abstract

A control system uses a feedforward neural network model to perform control of a steam turbine power system in sliding pressure mode in a more efficient and accurate manner than a control scheme that uses only a multivariate linear regression model or a manufacturer-supplied correction function. Turbine inlet steam pressure of a steam turbine power generation system in sliding pressure control mode has a direct one-to-one relationship with the electrical energy load (output) of the steam turbine power system. This new control system provides a more accurate representation of the turbine inlet steam pressure, such that the power generated by a power plant is more closely controlled to the target (demand). More particularly, the feedforward neural network model prediction of the turbine inlet steam pressure more closely fits with the actual turbine inlet steam pressure with very little error, and thereby providing better control over the electrical energy load.