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 A Neural Control of the Parallel Gas Turbine with Differential Link
Tác giả hoặc Nhóm tác giả: Nguyễn Hoàng Mai
Nơi đăng: IEEE International Conference on Systems Science and Engineering 2017; Số: 978-1-5386-3421-9;Từ->đến trang: 99-104;Năm: 2017
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Gas turbine engine has the highest performance in the engine rotation. The performance of the types of modern gas turbines could able up to 44%. In specific applications, gas turbines are used for equipment such as electrical generators, aircraft engines, high-speed boat ... The applications of gas turbine are used to transmit turbine, cabinet pull in general[3],[5],[6],[9],[10],[12],[15]. However, there have been no published works on the use of dual turbine. This paper presents an artificial neural network controller to control Double Differential Gas Turbine (DDGT) by use algorithm to synchronize the speed of two turbines at each variable turbine load to reduce low power balance in the system. From the Rowen’s model of control for a turbine, the author analyzed and combined with the existing model to construct a dual turbine combinatorial structure coupled by differential coupling. Model-driven control algorithms are used as training grounds for artificial neural networks (ANNs) to replace traditional PID controllers. Because the double tubine construction is strong nonlinear system, modeling is directly transformed from the object model. Simulation results for a dual-turbine twin-turbine combination of 32MW, demonstrating the suitability of the theory. Simulation results show that ANN can be deployed into practice to replace PID controllers to increase control accuracy.
ABSTRACT
Gas turbine engine has the highest performance in the engine rotation. The performance of the types of modern gas turbines could able up to 44%. In specific applications, gas turbines are used for equipment such as electrical generators, aircraft engines, high-speed boat ... The applications of gas turbine are used to transmit turbine, cabinet pull in eneral[3],[5],[6],[9],[10],[12],[15]. However, there have been no published works on the use of dual turbine. This paper presents an artificial neural network controller to control Double Differential Gas Turbine (DDGT) by use algorithm to synchronize the speed of two turbines at each variable turbine load to reduce low power balance in the system. From the Rowen’s model of control for a turbine, the author analyzed and combined with the existing model to construct a dual turbine combinatorial structure coupled by differential coupling. Model-driven control algorithms are used as training grounds for artificial neural networks (ANNs) to replace traditional PID controllers. Because the double tubine construction is strong nonlinear system, modeling is directly transformed from the object model. Simulation results for a dual-turbine twin-turbine combination of 32MW, demonstrating the suitability of the theory. Simulation results show that ANN can be deployed into practice to replace PID controllers to increase control accuracy.
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