Abstract
On account of the complexity of aero-derivative gas turbines and the much higher control requirements, it is significant and meaningful to design advanced controllers for obtaining the ideal control effect. In this paper, to improve the performance of the original controller of an aero-derivative gas turbine, a novel integrated control method is proposed by combining the original controller with a new neural network controller. It realizes the speed control by switching the two controllers during the operation process of the aero-derivative gas turbine. A tracking test and robustness test are conducted to assess the superiority of the novel integrated control method. The results show that in comparison with the original controller and the new neural network controller, the novel integrated control method has a much better speed tracking performance during the four tracking tests. When the model of the aero-derivative gas turbine changes with the ambient temperature and compressor performance degradation, the robustness of the novel integrated control method is also better than the other two controllers. Hence, the superiority of the novel integrated control method is validated.
Highlights
For aero-derivative gas turbines, control systems play a significant role in the operation process
The results show that in comparison with the original PID controller, the novel integrated controller decreases the overshoot, and improves the steady accuracy of this aero-derivative gas turbine
As the model of aero-derivative gas turbine changes with the increasing of the ambient temperature, the ICM still has an excellent tracking performance, which indicates that the ICM has the strongest robustness among the three control methods
Summary
For aero-derivative gas turbines, control systems play a significant role in the operation process. The results show that in comparison with the original PID controller, the novel integrated controller decreases the overshoot, and improves the steady accuracy of this aero-derivative gas turbine. What is more, this new control method is easier to implement in engineering, given the simple and highly efficient characteristics of this new neural network.
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