On-Line Component Map Adaptive Procedure Based on Sensor Data

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Abstract A key challenge in the gas turbine community is to adapt the engine model by matching measured data with simulation data. This study presents a procedure aiming to calibrate a certain type of gas turbine for power generation. To reproduce degradation, disturbance is injected into the healthy components maps at different time. Subsequently, six correction factors along with measured data and unmeasured parameters are coupled together using cooperative working equations and optimized based on primal-dual interior point method. When performing the adaptive procedure, Jacobian and hessian matrices are calculated using finite difference since the component maps have external, mapped, functions implemented as lookup-tables, and mode-switching statements. To improve the accuracy of first-order and second-order partial derivatives, the finite difference is enhanced by Richardson extrapolation method. The search scope of correction factors and unmeasured parameters are determined by the whole working conditions. Meanwhile, an adaptive update method of initial solution is proposed to make sure the convergence of the optimization procedure as quickly as possible. Finally, the proposed method is further applied to the on-line adaptation in case of performance degradation. The influence of measurement noise on optimization is also studied. It is demonstrated that the procedure is capable of refining the component maps progressively, which is significant for the model-based gas path diagnostics and prognostics.