Simulation and analysis of humid air turbine cycle based on aeroderivative three-shaft gas turbine

Due to the fact that the turbine outlet temperature of aeroderivative three-shaft gas turbine is low,the conventional combined cycle is not suitable for three-shaft gas turbines.However,the humid air turbine(HAT)cycle provides a new choice for aeroderivative gas turbine because the humidification process does not require high temperature.Existing HAT cycle plants are all based on single-shaft gas turbines due to their simple structures,therefore converting aeroderivative three-shaft gas turbine into HAT cycle still lacks sufficient research.This paper proposes a HAT cycle model on a basis of an aeroderivative three-shaft gas turbine.Detailed HAT cycle modelling of saturator,gas turbine and heat exchanger are carried out based on the modular modeling method.The models are verified by simulations on the aeroderivative three-shaft gas turbine.Simulation results show that the studied gas turbine with original size and characteristics could not reach the original turbine inlet temperature because of the introduction of water.However,the efficiency still increases by 0.16%when the HAT cycle runs at the designed power of the simple cycle.Furthermore,simulations considering turbine modifications show that the efficiency could be significantly improved.The results obtained in the paper can provide reference for design and analysis of HAT cycle based on multi-shaft gas turbine especially the aeroderivative gas turbine.

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Since gas turbine plays a key role in electricity power generating,the requirements on the safety and reliability of this classical thermal system are becoming gradually strict.With a large amount of renewable energy being integrated into the power grid,the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines.The startup,shutdown,and load fluctuation are dominating the operating condition of gas turbines.Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design,operation,and maintenance.However,conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations.Although data-driven simulating methods,to some extent,can mitigate the problem,it is impossible to perform simulations with insufficient data.To tackle the issue,a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data.A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring.The simulation accuracy is significantly increased by 24.6%and the predicting error reduced by 63.6%compared with the baseline model.Moreover,compared with the other classical transfer learning modes,the method proposed has the best simulating performance on field testing data set.Furthermore,the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.