Research On Modeling And Predictive Control Of Heavy Duty Gas Turbine Based On Neural Network

Highly nonlinear couple relationship among parameters of heavy-duty gas turbine confronts traditional mechanism modeling and control design with significant difficulty.The method of modeling based on neural network,which achieves the modeling work by black box principle,provides a new way to solve the problem.The generated gas turbine neural network model can be inserted into predictive control algorithm,inspiring a new way to achieving predictive control.In this paper,two types of forward dynamic network,NARX neural network and Elman network,were utilized to model heavy-duty gas turbine start-up transient condition on the basis of operation data,for the purpose of predicting operating parameters.Testing result revealed that,reasonable configuration of number of hidden layer neurons can all lead to acceptable prediction models.For NARX network,parallel operation mode has higher application value.But its prediction error is larger than series-parallel operation mode’s.The individuals which have better performance in series-parallel operation mode cannot promise better performance in parallel operation mode.Because of the difference between training error function and testing error function,under the circumstance of requiring higher prediction performance in particular part of process,gradient descent direction of network training can be guided by revising training error function.In this paper,training samples which have higher numerical value output parameters were assigned greater weight in training error function,to acquire network individuals whose performance in high numerical value zone was better,at the price of performance in low numerical value zone.As for the issue of multiple training sample sequences,split joint and zeroing the weight of junction operation in network training can acquire better network models and simplify training step,compared the method of training in turns and weighting the outputs of several networks.Elman network cannot work in series-parallel mode,but has better performance in whole sequence prediction compared to NARX network.Because of the convergence problem of context layer input parameters,Elman network is overwhelmed by NARX network in first 10 steps if the start point of testing does not match the initial state of training sequence.After the test of gas turbine dynamic prediction,NARX network model was inserted into nonlinear predictive control algorithm,playing the role of nonlinear prediction model in the algorithm.By combining neural network prediction model with heat transfer search receding horizon optimization algorithm and objective function,multi-input-multi-output predictive control of a heavy-duty gas turbine Simulink model object was achieved in simulation test.When the peak regulation task was completed,the constrain of turbine exhaust temperature was given consideration at the same time.In the method of regulating the term of turbine exhaust temperature in objective function,load increasing process can be control in cycle efficiency priority mode and load increasing rate priority mode,proving the flexibility of this predictive control design.Moreover,this predictive control method has the ability of rejecting disturbances to some extent,which was verified by test.