Identification of Control State Changes in a Power Plant Desuperheater System via Transfer Functions and Gaussian Process Modeling

Abstract This work presents a novel study for identifying alterations in the control states of a desuperheater system based on real closed-loop data from a coal-fired power plant operating under various loads using linear and nonlinear system identification techniques. Specifically, Transfer Functions (TFs) and Gaussian Processes within a Nonlinear AutoRegressive eXogenous model (GP-NARX) are utilized. The desuperheater system comprises two units, north and south, each modeled as a single-input single-output (SISO) system based on spray valve positions and outlet temperatures. To identify changes in the control states using TFs, deviations in the coefficients of three poles and two zeros transfer functions are analyzed. Significant shifts in the control states of the north desuperheater are observed when transitioning from nominal to half and low loads, with deviations of up to four orders of magnitude. Substantial changes in control states are also observed for the south desuperheater when moving from nominal to low load, with a deviation in the coefficients of up to five orders of magnitude, whereas the transition from nominal to half load shows a smaller deviation of up to three orders of magnitude. In the GP-NARX approach, model uncertainties are used to indicate the changes in the control states. The south desuperheater showed a significant uncertainty of up to 8°F from the nominal to the low load, evidencing a change in the control states. Regarding the north desuperheater, increased uncertainty, up to 6°F, is also observed but in shorter time intervals when compared to the south desuperheater. Ultimately, this work shows that both approaches can be used as a basis for system identification, employing real closed-loop power plant data.