A nonlinear neural network-based model predictive control for industrial gas turbine

Abstract

Gas turbines are now extensively used in aviation, oil and gas applications and power generation. With this increasing use in a diverse range of applications, gas turbine engines are designed to operate in a wide operating envelope. Typically, the ambient temperature can vary substantially from a hot summer day to a cold winter night. In addition, different fuel types may be used. Furthermore, the performance of a turbine engine deteriorates with use because of component degradation caused by erosion and corrosion. These requirements for guaranteed high performance levels while maintaining stability and safe operation with minimum overall cost impose severe challenges on control system design. In this dissertation, new approaches for gas turbine engine modelling and multivariable advanced controller design are investigated. A nonlinear model predictive control (NMPC) approach based on an ensemble of recurrent neural networks (NN) is utilized to achieve the control objectives for a Siemens SGT-A65 three spool aeroderivative gas turbine engine used for power generation. A novel ensemble method is proposed, which results in an adaptive NN model. The simulation results show improvement in accuracy and robustness by using the proposed modelling approach. Also, another important gain is the very rapid execution time (40,5 μs), which can support many real time applications that require model based control design. For the closed-loop control, a constrained multi-input multi-output (MIMO) nonlinear model predictive controller (NMPC) is developed based on the generalized predictive control (GPC) algorithm because of its ability to handle MIMO problems in one algorithm. In this controller, a novel trade-off approach between the usage of a non-linear model and successive linearization approaches is used in order to reduce the computation effort and at the same time increase the robustness of the controller. Estimation of the free and forced responses of the GPC are performed based on the NN model of the plant at each sampling time. In addition, the Hildreth’s Quadratic Programming (QP) procedure is utilized to solve the quadratic optimization problem of the NNGPC controller, which offers simplicity and reliability in real-time implementation. A comparison between the performance of the proposed controller (NNGPC) and the current controller of the SGT-A65 engine (min-max controller) is performed. The simulation results show that the NNGPC has demonstrated superior output responses with less oscillatory behavior and smoother control actions to sudden variations in the electric load than those observed in the existing min-max controller. Furthermore, the NNGPC controller requires less control effort than the min-max controller to achieve the desired objectives. The minimization of control effort has significant practical repercussions because it reduces the intensity of mechanical wear of the actuators, which leads to an increase in the functional safety, lifetime, and economics of the controlled process. In addition, the computation time required to solve an optimization problem was sufficiently shorter than the sampling period which makes a real-time implementation of the NNGPC controller possible.

Date	22 Oct 2020
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Ouassima Akhrif (Supervisor) & Saïd Hany Moustapha (Co-supervisor)