Online Prediction of Temperature and Stress in Steam Turbine Components Using Neural Networks

Abstract

Considered here are nonlinear autoregressive neural networks (NETs) with exogenous inputs (NARX) as a mathematical model of a steam turbine rotor used for the online prediction of turbine temperature and stress. In this paper, the online prediction is presented on the basis of one critical location in a high-pressure (HP) steam turbine rotor. In order to obtain NETs that will correspond to the temperature and stress the critical rotor location, a finite element (FE) rotor model was built. NETs trained using the FE rotor model not only have FEM accuracy but also include all nonlinearities considered in an FE model. Simultaneous NETs are algorithms which can be implemented in turbine controllers. This allows for the application of the NETs to control steam turbine stress in industrial power plants.

An online monitoring method for creep-fatigue life consumption with real-time damage accumulation

“…The problem of calculating stress responses based on monitored measurable data is not discussed in this study. Many studies have provided applicable methods through Green's function (Banaszkiewicz, 2016), transfer function (Sun et al, 2013), and neural networks (Dominiczak et al, 2015), etc.…”

Advanced gas turbines health monitoring systems

“…Artificial Neural Networks (ANN) have been a powerful tool in estimating gas turbines performance [17][18][19][20][21][22][23][24][25][26][27] and fault detection due to their ability to model highly nonlinear relationship such as fuel flow and shaft speed dynamics. They prove to have excellent approximation capabilities.…”

“…In this paper, the ANN proves a good accuracy of estimation but also better calculation performance over the physical simulator. A stress control method employing an ANN is investigated in [20]. Data required to carry out network training in this instance is obtained from a fine element model as stress measurements on the turbine rotors are not available.…”

Experimental evaluation of the dynamic properties of an energy microturbine with defects in the rotating system

“…In addition to a good dynamic performance of the rotor over a wide rotational speed range, the bearing systems must be of great durability and reliability [7], allowing microturbines to operate without constant technical supervision. With the compact dimensions and low power output of microturbines, no strict procedures have to be followed every time they are set in motion as opposed to big power-station turbines [5]. As microturbine blades are small and rigid, they do not suffer from dynamic problems generally encountered in turbomachines used in the power industry [16].…”