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

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Abstract

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.

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Abbreviations

R g :

Gas constant

T :

Gas temperature/K

V :

Volume/m3

HV :

Fuel heating value/(kJ · kg−1)

h :

Enthalpy/(kJ·mol−1)

c p,g :

Heat capacity/(J·K−1)

ρ :

Combustor gas density/(kg·m−3)

NET sim :

Neural networks trained by simulation data set

NET real :

Neural networks trained by real-world data set

d :

Distance metrics

MSE:

Mean square error

Error :

Relative error

I :

Inertia moment/(kg·m−2)

P :

Pressure/Pa

N :

Rotation speed/(r·min−1)

D :

Data set

X :

Input signal tensor of simulation model

Y :

Output signal tensor of simulation model

V :

Encoded vector

L :

Latent vector

W :

Parameters of neural networks

β :

Weighting factor

R 2 :

R2 score for regression

t:

Turbine

c:

Compressor

g:

Gas

in:

Inlet

out:

Outlet

GG:

Gas generator

PT:

Power turbine

1:

Inlet of the gas generator

2:

Inlet of the combustor

3:

Inlet of the high pressure turbine

34:

Outlet of the gas generator

4:

Outlet of the power turbine

field:

Field data

sim1:

Field signal-simulation data

sim2:

Transient process simulation data

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51706132 and 51876116), Aeronautical Science Foundation of China (Grant No. 2017ZB57003), National Science and Technology Major Project (Grant Nos. 2017-I-0002-0002 and 2017-I-0011-0012), and National Fundamental Research Project (Grant No. 2019-JCJQ-ZD-133-00).

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Authors and Affiliations

  1. Key Laboratory of Power Machinery and Engineering (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200240, China

    Dengji Zhou, Jiarui Hao, Dawen Huang, Xingyun Jia & Huisheng Zhang

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  1. Dengji Zhou
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  2. Jiarui Hao
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  3. Dawen Huang
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  4. Xingyun Jia
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  5. Huisheng Zhang
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Correspondence to Huisheng Zhang.

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Zhou, D., Hao, J., Huang, D. et al. Dynamic simulation of gas turbines via feature similarity-based transfer learning. Front. Energy 14, 817–835 (2020). https://doi.org/10.1007/s11708-020-0709-9

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