Citation: Guo, S.; He, A.; Xiao, B.;
Liu, P.; Wang, Z. A Novel Integrated
Control Method for an
Aero-Derivative Gas Turbine of
Power Generation. Machines 2022, 10,
179. https://doi.org/10.3390/
machines10030179
Academic Editors: Kelvin K.L. Wong,
Dhanjoo N. Ghista, Andrew W.H. Ip
and Wenjun (Chris) Zhang
Received: 19 January 2022
Accepted: 25 February 2022
Published: 28 February 2022
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Article
A Novel Integrated Control Method for an Aero-Derivative Gas
Turbine of Power Generation
Senchuang Guo
1,2,3,4
, Ai He
5
, Bo Xiao
1,2,3,4,
*, Peijun Liu
1,2,3,4
and Zinan Wang
1,2,3,4
1
Advanced Gas Turbine Laboratory, Institute of Engineering Thermophysics, Chinese Academy of Sciences,
Beijing 100190, China; guosenchuang@iet.cn (S.G.); liupeijun@iet.cn (P.L.); wangzinan@iet.cn (Z.W.)
2
Innovation Academy for Light-Duty Gas Turbine, Chinese Academy of Sciences, Beijing 100190, China
3
Key Laboratory of Advanced Energy and Power, Institute of Engineering Thermophysics,
Chinese Academy of Sciences, Beijing 100190, China
4
School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100190, China
5
China United Gas Turbine Technology Co., Ltd., Beijing 100016, China; heai0225@foxmail.com
* Correspondence: xiaobo@iet.cn
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.
Keywords:
aero-derivative gas turbine; speed control; original controller; neural network controller;
integrated control method; performance analysis
1. Introduction
For aero-derivative gas turbines, control systems play a significant role in the operation
process. The traditional proportional-integral-derivative (PID) controller has a simple
structure, high reliability, and easy operation, and is the widest used controller. However,
it also has the weakness of a large overshoot and is more suitable for the linear control
task. Firstly, for aero-derivative gas turbines, if the overshoot is too large, there will be
a risk of over-speed, and even rotor fracture, which will damage the entire gas turbine
unit. In addition, the aero-derivative gas turbine has a complex structure, strong coupling,
nonlinearity, and uncertainty. It has also been witnessed to have much higher control
requirements, which include high efficiency, high flexibility, and high reliability. Hence,
an advanced controller is indispensable for the purpose of obtaining the perfect control
performance. Recently, multiple scholars have introduced neural network, fuzzy logic, and
model predictive control, etc., to develop advanced controllers of gas turbines. Meanwhile,
in contrast to fuzzy logic and model predictive control, neural networks have received
the fastest development and application on account of their advantages [
1
,
2
]. Moreover,
neural network technology was applied to the Jebel Ali gas turbine plant in 2019, and the
obvious performance improvement was achieved [
3
]. Currently, the conducted research
on neural network technology in gas turbine control through surveys can be divided into
three aspects.
Machines 2022, 10, 179. https://doi.org/10.3390/machines10030179 https://www.mdpi.com/journal/machines
