Abstract
Solar Thermal Power (STP) plants are a crucial technology for converting solar energy into electricity. Among the STP, the Parabolic Trough Collector (PTC) is one of the integral parts of STP systems for electrical power generation. Maximizing PTC performance is a challenge due to dynamic and unpredictable solar radiation and environmental disturbances, such as cloud cover and dust accumulation. The optical efficiency parameter of the PTC is critical for calculating the desired heat gain, which is difficult to measure in real-time, necessitating sophisticated control and estimation strategies for optimal heat regulation. This study introduces two control mechanisms for the PTC system and comparing their efficacy. A classical PI controller supplemented by Static Feed-Forward (SFF) control demonstrates improved performance with an optimal transfer function model. In contrast, Nonlinear Model Predictive Control (NMPC) excels in disturbance rejection and setpoint tracking, considering operational constraints. The NMPC controller notably outperforms the PI controller with SFF in performance metrics, with the Integral Time Squared Error (ITSE) decreasing by approximately 99% across case studies. Also, in the case of heat gain, NMPC controller exhibits percentage increases when compared to the PI controller. Furthermore, since the measurement of optical efficiency is essential, but due to difficulty in the measurement for various reasons, the estimator is used as a virtual sensor to estimate those optical efficiency. The unmeasured states and parameters, including optical efficiencies of the PTC, are estimated using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) techniques, with UKF exhibiting superior accuracy. However, considering the average computation time, EKF is preferred for real-time applications.
Published Version
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