Nonlinear Adaptive Control of Heat Transfer Fluid Temperature in a Parabolic Trough Solar Power Plant
Abstract
:1. Introduction
2. Design
2.1. Adaptation
2.2. Prediction
- The controller output at time k, , must be the first value of a predicted controller output sequence in which all the increments in the interval are equal to zero. In the same interval, all the increments of the predicted disturbance sequence are assumed to be equal to zero.
- The predicted process output trajectory must be at time equal to a desired output value .
2.3. Initialization
- A generic, critically damped, second order linear model is chosen. A high gain of this model is desirable since it will avoid abrupt control actions during the first control instants.
- The controller is then started with the RBF network disabled and is updated at every control step according to Equation (6).
- After the linear model has somehow stabilized, it is set as and the regular operation described above in this section is started.
2.4. Sequence of Operations
- Obtain a measurement of the process output , along with a measurement of the variable vector .
- Calculate the RBF network value at , , by using Equation (4).
- Obtain the expected process model linearization at , according to Equation (3).
- Obtain the new expected process model linearization at , according to Equation (3).
- Calculate the desired output process trajectory by using Equation (9).
- Calculate the control sequence as described in Section 2.2.
- Apply the first element of the calculated control sequence.
- Wait for the selected control period and repeat this sequence from the beginning.
3. Results and Discussion
3.1. Simulation
3.2. Control Scenario
3.3. Controller Configuration and Start-Up
- The controller sampling time has been set to 180 s.
- The variable under control, or process output, referred to as y in Equation (2) corresponds to the HTF final temperature and its set point has been set to 393 C.
- The control output, or process input, referred to as u in Equation (2) corresponds to the HTF pumps flow rate control loop set point in kg/s.
- The control action incremental limit has been set to 300 kg/s.
- The measurable disturbance referred to as d in Equation (2) corresponds to the measured DNI in W/m.
- The model size in Equation (2) has been set with the values , and .
- The horizon length referred to as in Equation (2) has been set to 7.
- The model parameters dependency set of variables referred to as in Equation (2) comprises only the circulating HTF mass flow rate as .
3.4. Performance Comparison
3.4.1. Regular Operation
3.4.2. Fault Tolerance
3.5. Future Works
4. Conclusions
Author Contributions
Conflicts of Interest
References
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Variance | |||||
---|---|---|---|---|---|
Linear model | 2.911 | 3.506 | 2.748 | 4.944 | 40.751 |
Nonlinear model | 2.407 | 2.361 | 2.355 | 2.338 | 2.336 |
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Nevado Reviriego, A.; Hernández-del-Olmo, F.; Álvarez-Barcia, L. Nonlinear Adaptive Control of Heat Transfer Fluid Temperature in a Parabolic Trough Solar Power Plant. Energies 2017, 10, 1155. https://doi.org/10.3390/en10081155
Nevado Reviriego A, Hernández-del-Olmo F, Álvarez-Barcia L. Nonlinear Adaptive Control of Heat Transfer Fluid Temperature in a Parabolic Trough Solar Power Plant. Energies. 2017; 10(8):1155. https://doi.org/10.3390/en10081155
Chicago/Turabian StyleNevado Reviriego, Antonio, Félix Hernández-del-Olmo, and Lourdes Álvarez-Barcia. 2017. "Nonlinear Adaptive Control of Heat Transfer Fluid Temperature in a Parabolic Trough Solar Power Plant" Energies 10, no. 8: 1155. https://doi.org/10.3390/en10081155