Development and implementation of a model predictive control system for a solar parabolic trough plant influenced by...

Renewable energy is critical to conserve resources and provide a secure environment for generations to come, and the most common and abundant of these is solar energy. India hopes to reach 500 gigawatts of non-fossil fuel power by this decade, and solar systems are likely to contribute prominently to this goal. Solar based generation is clean, pollution free, and important for national energy security, while solar thermal collectors form the core of such systems. A widely used concentrating solar power technology is the parabolic trough collector, which shows nonlinear behaviour and is disturbed by changes in input energy, requiring advanced control methods. A weather disturbance model was developed in this study using local data from Tamil Nadu, India, and evaluated under three control schemes: Predictive functional control, proportional-integral-derivative, and model predictive control. The findings indicate that model predictive control regulates outlet temperature more effectively, limiting peak overshoot to 6.67% with a settling time of 3600 s. In contrast, predictive functional control recorded 25% overshoot, while proportional-integral-derivative control showed 43.33% overshoot. The state-space-based weather disturbance model also provided closer tracking of set values compared with the other approaches. Predictive functional control produced a quicker transient response but with significant overshoot, the proportional-integral-derivative scheme delayed in reaching the target, while model predictive control maintained steady and consistent stability. All controllers had a delay of about 30 to 60 s, but model predictive control provided the best overall balance of disturbance rejection, settling time, and tracking accuracy.