Summary

This thesis has investigated the use of advanced control systems and the specific application on superheated steam temperature control through spray water quenching. This was done with the specific purpose of gaining a performance increase compared to the widely used PID control technique that is currently also in use on this system.

Through the study of literature surrounding superheater temperature control, specifically in boilers, two techniques were singled out for further research. The MPC and Fuzzy control techniques are supported by other researchers, for the control of superheater temperature with spray water quenching. Fuzzy and MPC control solutions was investigated further as a solution for better performing temperature control. Literature review also highlighted several techniques available to attempt to control for plant non-linearity. This included controlling with a variable sample time depending on the system parameters. Alternatively, Fuzzy control also has the built- in ability to facilitate control over a range of variable plant dynamics.

Process analysis was presented in as much as the superheater system fits into the entire power generation process. The particulars of boiler operations at Komati Power Station were investigated with specific emphasis on known non-linearity, process dynamics, disturbances as well as the interface between various other control systems in the process.

The control strategy for the superheater itself is in fact also a cascade controller consisting of an outer control loop feeding into an inner loop to control the inlet temperature to the final superheater. This is done to achieve the desired outlet steam temperature exiting the final superheater. The inner loop is a trivial control loop that applies the spray water necessary to reach the desired inlet steam temperature. It is trivial in the sense that it is a much faster control loop

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compared to the outer loop. It does not suffer from any significant non-linearity that hamper its operation. The study focuses on optimisation of the outer control loop.

Practically, many disturbances are in-fact due to the parallel operation of process controllers that control the system as a whole. The reason behind the segregation of these processes and controllers were discussed, while the need was established to still include the outputs of these controllers into the superheater steam temperature control.

The controller’s operating range is also investigated, as part of the operating envelope for the process, and non-linearities were identified. The system dependence on the steam flow rate was a major non-linearity that needed to be addressed to ensure adequate operation across the entire allowable range. Secondly, the fact that this is a thermodynamic process that operates across various temperatures and pressures, also results in non-linearity in the process model. The combustion process itself is highly variable with too few measurements to accurately model. This contributes to uncertainty in the process model.

System identification was then performed using a large variety of on-load operation data of a particular generating unit at Komati Power Station. The basic model, fit for purpose, was identified through literature review, and adapted for this application. The model was fitted for the particulars of this project with the data gathered from the unit. A state space model was constructed taking firing rates, superheater-inlet steam temperature and -outlet steam temperature and pressure as inputs. The state space model was linearized at full load. Through the application of a variable sample time, it was shown that the model dependency on the steam flow rate could be mitigated.

The combustion process was not modelled directly, rather a pre-filter was constructed to be applied to the firing rate measurements that feed the combustion process. An iterative least squares regression method was used to fit the model to the available data. The model response was compared to the actual plant response. Model deviations from the actual plant would also later be used to construct an unmeasured disturbance signal to be used during simulation.

A cost function for the MPC controller was constructed using the single system input and was combined with the measurable disturbances. An optimised control law was then constructed from

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the cost function at its minimum. A reduced order observer was also constructed to estimate the unmeasured system state. The MPC control law is intended to be applied at a variable sample rate based on the operating load condition, as measured by steam throughput.

The fuzzy controller was based on Takagi-Sugeno-Kang Fuzzy control architecture where optimised controllers are designed for each rule-based region. Each rule was based on the steam flow rate (high and low) and acceleration of steam (positive, small and negative) creating a two- dimensional region map with overlapping triangular membership functions.

The controller for each region was optimised with a fixed sample time based on the original model. An adapted cost function was used to determine feedback gains for the states and measured disturbances. The system response takes advantage of the fact that steam flow acceleration, is generally limited, and compensates for the change in plant dynamics, as the flow rate is expected change. This compensation is encapsulated in the cost function.

The two control techniques were evaluated through simulation. The model used for simulation was also disturbed with an unmeasured disturbance to mimic the model error that was remaining after system identification. 5 case studies were taken from the 38 datasets for detailed discussion, as well as average performance indicators for all the results.

The MPC controller proved during simulation to reduce the standard deviation from setpoint by 54 % compared to the current PID controller response. It also reduced the maximum and minimum deviations from setpoint by 49 % and 29 % respectively. Qualitatively it can also be said that the incorporation of measured disturbances in the controller feedback significantly reduce the reaction time of the controller specifically to those disturbances.

The MPC controller was also shown to avoid two tripping scenarios, in which the PID controller had failed to maintain operating parameters within safe limits. The same positive result was also achieved when simulating the Fuzzy controller.

The fuzzy controller achieved results similar to that of the MPC controller, when compared to the PID controller. It reduced the standard deviation from setpoint by 57 %, and the maximum and minimum deviations from setpoint by 50 % and 55 % respectively. In general, the Fuzzy

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controller out-performed the MPC controller, but especially in areas of high non-linearity, with a high absolute acceleration in steam flow.

Both controllers were then tested on the operations simulator’s perturbed representation of the plant in question. The results showed that although the emulated plant had significantly higher gain and larger time constant, the MPC controller performed at least comparably well against the PID controller.

The MPC controller suffered from overshoot, but generally returned back to setpoint much faster. Some oscillations were still present in the response. In both test cases the controller settled at least as fast as the PID controller. Under these conditions there were positive and negative aspects of the responses. The test also pointed out that the controller responded within the operating envelope for a perturbed plant and shows some degree of robustness.

The fuzzy controller outperformed the PID and MPC controllers again during testing on the operations simulator. It settled significantly faster and reduced the peak temperature responses to a large extent. Again, there is some oscillation before settling out due to the perturbation of the plant. Nonetheless, he controller performed significantly better than the MPC and PID controllers and is shown to be robust enough for general application between the varying units at Komati Power Station.