A component map adaptation method for compressor modeling and diagnosis

Energy Science & Engineering

Wang

2022

Self Cite

To extend useful life, improve availability, and reduce gas turbine maintenance costs, scholars have proposed many gas‐path fault diagnostic approaches on the basis of steady‐state operating conditions. However, gas turbines increasingly have to operate more flexibly in grid‐supported modes. In transient operating mode, the service life of the gas turbine will be consumed faster compared with that in base‐load operating mode. In addition, the use of a variable geometry compressor has introduced new uncertainty in the actual diagnostic process, making the applicable boundaries of the original gas‐path diagnosis methods narrower and narrower. Regarding the issue above, a novel approach is proposed for fault diagnosis of gas turbine gas‐path components in power plants under a transient operating condition with the variable geometry compressor for the first time. First, the mathematical relationship of the influence of the compressor inlet guide vane position on the compressor flow and efficiency characteristics is deduced. And a high‐precision thermodynamic model for the purpose of performance analysis and gas‐path fault diagnosis is established, which comprehensively considers the above interference factors and includes health parameters of the main gas‐path components. Second, the thermodynamic model‐based diagnosis strategy under transient operating conditions with the variable geometry compressor is proposed and component health parameters can be quantitatively obtained. At last, the effectiveness and reliability of the method proposed are validated through both simulation test and actual operation test with good accuracy and real‐time performance to diagnose single and multiple component performance degradations, and component abrupt fault.

Section: Results and Analysismentioning

confidence: 99%

Gas turbine gas‐path fault diagnosis in power plant under transient operating condition with variable geometry compressor

Energy Science & Engineering

Wang

2022

Self Cite

“…In our previous work, 19,20 two thermodynamic models of single-shaft power generation gas turbines were developed by the MATLAB/Simulink software platform to test the effectiveness of the method put forward in this paper. One of them is a transient or dynamic heat gas turbine model, as shown in Figure 6.…”

Section: Application and Analysismentioning

confidence: 99%

Long-short term memory and gas path analysis based gas turbine fault diagnosis and prognosis

Zhou

Advances in Mechanical Engineering

et al. 2021

Self Cite

At present, the main purpose of gas turbine fault prediction is to predict the performance decline trend of the whole system, but the quantitative and thorough performance health index (PHI) research of every major component is lacking. Regarding the issue above, a long-short term memory and gas path analysis (GPA) based gas turbine fault diagnosis and prognosis method is proposed, which realizes the coupling of fault diagnosis and prognosis process. The measurable gas path parameters (GPPs) and the health parameters (HP) of every main component of the goal engine are obtained through the adaptive modeling strategy and the gas path diagnosis method based on the thermodynamic model. The predictive model of the Long-Short Term Memory (LSTM) network combines the measurable GPPs and the diagnostic HPs to predict the HPs of each major component in the future. Simulation experiments show that the proposed method can effectively diagnose and predict detailed, quantified, and accurate PHIs of the main components. Among them, the maximum root mean square error (RMSE) of the diagnosed component HPs do not exceed 0.193%. The RMSE of the best prediction model is 0.232%, 0.029%, 0.069%, and 0.043% in the HP prediction results of each component, respectively.

“…Firstly, based on the engine boundary condition parameters u and gas path measurable parameters Z from the target engine at newly commssioning stage when the engine is always healthy and clean, the gas turbine thermodynamic model of the object to be diagnosed can be constructed by adaptation modeling strategy. And the specific principle of adaptation modeling strategy can be referred to the our previous research work [34]. And then the gas turbine thermodynamic model can be taken as a basal model to simulate various gas path faults.…”

Section: Fig7 the Thermodynamic Working Principle Diagram Of This Heavy-duty Enginementioning

confidence: 99%

Gas Path Fault Diagnosis of Gas Turbine Engine Based on Knowledge Data-Driven Artificial Intelligence Algorithm

Jin

et al. 2021

IEEE Access

Self Cite

As the core power for the aviation industry, shipbuilding industry, and power station industry, it is essential to ensure that the gas turbines operate safely, reliably, greenly and efficiently. Learn from the advantages and disadvantages of the thermodynamic model based and data-driven artificial intelligence based gas-path diagnosis methods, a newfangled gas turbine gas-path diagnosis approach on the basis of knowledge data-driven artificial intelligence is proposed. That is a hybrid method of deep learning and gas path analysis. First, gas turbine thermodynamic model of the object to be diagnosed is constructed by adaptation modeling strategy. And the engine thermodynamic model is taken as the basal model to simulate various gas path faults. Secondly, a large number of knowledge data corresponding to component health parameters and gas turbine boundary condition parameters & gas-path measurable parameters are simulated by setting different component health parameter values and different boundary conditions based on this basal model. And next, define the vector composed of the boundary condition parameters & the gas path measurable parameters in the knowledge database as the input vector, and the component health parameter vector as the output vector, and a deep learning model for regression modeling of this knowledge database is designed. At last, along with the gas turbine engine runs, the trained model outputs component health parameters in real time after trained deep learning model is deployed to the corresponding gas turbine power plant. The simulation experiment results show that, accurate and quantified health parameters of each gas path component can be obtained by the proposed method in this paper, and the overall root mean square error does not exceed 0.033%, and the maximum relative error does not exceed 0.36%, which illustrates the proposed method has great application potential. INDEX TERMSGas turbine; thermodynamic model; component health parameter; gas path analysis; deep learning; SYMBOLS AND ABBREVIATIONS GPA Gas path analysis EOH Equivalent operating hour GRNN Generalized regression neural network BP Back propagation neural network RBF Radial basis function neural network DNN Deep learning model ANN Artificial neural network FC Fully connected layer SF Component health parameters u Boundary condition parameters Z Gas-path measurable parameters