Browsing by Author "Usen, Francis Francis"

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    Design and simulation of kalman filter for estimation of gas turbine inlet temperature
    (Federal University of Technology, Owerri, 2024) Usen, Francis Francis
    The measurement of Gas Turbine (GT) Inlet Temperature remains a significant challenge for engineers, particularly in developing countries, due to the specialized technology required for accurate estimation and covariance noise signal attenuation in remote temperature measurement systems. This technology is primarily utilized by Gas Turbine manufacturers, who employ proprietary, closed-source mathematical models that are inaccessible to external engineers. To address this limitation, the present study develops an open-source model capable of both estimating GT Inlet Temperature and mitigating noise characteristics in the measurement data. The approach is based on the integration of a Kalman Filter (KF) model and a Plant model within a State-Space framework, utilizing real-time input parameters from two identical Gas Turbines, GT11 and GT12, designed by Asea Brown Boveri (ABB). The primary objective is to ensure that the proposed open-source model delivers optimal performance and solution accuracy comparable to that of the closed-source proprietary models. Initially, the Burner Can Temperature Rise Equation is employed to compute the GT Inlet Temperatures directly for the two turbine models. This equation is subsequently used to derive the system matrices in the State-Space representation, which describe the plant model. To complete the modelling, fictitious noise signals are introduced into the plant model and superimposed onto the Kalman Filter model to simulate real-world measurement conditions. The resulting design is implemented and tested in the MATLAB Simulink environment. Simulation results demonstrate that the proposed open-source model achieves accuracies of 98.1% and 97.2% for GT11 and GT12 respectively, when compared to real-time process data from ABB, while the calculated values yield 80% and 65% accuracies, respectively. Furthermore, the fictitious covariance noise signals were successfully filtered from the temperature measurements, confirming the robustness of the proposed model in mitigating noise and enhancing temperature estimation accuracy.