Classification and prediction of gas turbine gas path degradation based on deep neural networks

Cao, Qiwei; Chen, Shiyi; Zheng, Yingjiu; Ding, Yongneng; Tang, Yin; Huang, Qin; Wang, Kaizhu; Xiang, Wenguo

doi:10.1002/er.6539

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…Supporting this observation, Table 2 presents the evaluation parameter values for both the training and testing data, with the training data showing a lower RMSE value, aligning with the anticipated outcomes. The fact that these numbers are less than 0.5 shows the better per formance of this model than other research with the same purpose [21,30,39,40]. Upon comparing the two outcomes, it was found that the accuracy of the model is better for the training data than the testing data.…”

Section: Resultsmentioning

confidence: 75%

Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

Pasandideh,

Taylor,

Tito

et al. 2024

Energies

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This study focuses on using machine learning techniques to accurately predict the generated power in a two-stage back-pressure steam turbine used in the paper production industry. In order to accurately predict power production by a steam turbine, it is crucial to consider the time dependence of the input data. For this purpose, the long-short-term memory (LSTM) approach is employed. Correlation analysis is performed to select parameters with a correlation coefficient greater than 0.8. Initially, nine inputs are considered, and the study showcases the superior performance of the LSTM method, with an accuracy rate of 0.47. Further refinement is conducted by reducing the inputs to four based on correlation analysis, resulting in an improved accuracy rate of 0.39. The comparison between the LSTM method and the Willans line model evaluates the efficacy of the former in predicting production power. The root mean square error (RMSE) evaluation parameter is used to assess the accuracy of the prediction algorithm used for the generator’s production power. By highlighting the importance of selecting appropriate machine learning techniques, high-quality input data, and utilising correlation analysis for input refinement, this work demonstrates a valuable approach to accurately estimating and predicting power production in the energy industry.

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Section: Resultsmentioning

confidence: 75%

Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

Pasandideh,

Taylor,

Tito

et al. 2024

Energies

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“…The results of the proposed NARX model validated the capability of the NARX NN in determining the dynamic behavior of the gas turbine system, with a simulation MSE of 3.8414 × 10 −3 for the high pressure (HP) turbine, 1.29152 × 10 −1 and 2.12090 × 10 −4 for the gas and air control valves, respectively. In terms of deep learning, Cao et al (2021) [18] have presented different deep learning techniques that have been used to predict the changes in the efficiency and flow capacity of turbomachinery. The degradation predictions have been established via the LSTM approach, with a high accuracy ranging from 81.65% to 93.65%.…”

Section: Related Work and The Paper Contributionmentioning

confidence: 99%

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

2022

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Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network (CNN). The outputs selected for simulations are: the output power, the exhausted temperature and the turbine speed or system frequency, whereas the inputs are the natural gas (NG) control valve, the pilot gas control valve and the compressor variables. The data-sets have been prepared in three essential formats for the training and validation of the networks: normalized data, standardized data and SI units’ data. Rigorous effort has been carried out for wide-range trials regarding tweaking the network structures and hyper-parameters, which leads to highly satisfactory results for both models (overall, the minimum recorded MSE in the training of the MISO NARX was 6.2626 × 10−9 and the maximum MSE that was recorded for the MISO CNN was 2.9210 × 10−4, for more than 15 h of GT operation). The results have shown a comparable satisfactory performance for both dynamic NARX ANN and the CNN with a slight superiority of NARX. It can be newly argued that the dynamic ANN is better than the deep learning ANN for the time-based performance simulation of gas turbines (GTs).

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“…In recent years, the gas turbine-combined cycle (GTCC) has become increasingly popular in the power industry for its high efficiency and low pollutant emissions [1][2][3]. The GTCCs are more frequently used for peak shaving applications in some countries, for example, in China, requiring GTCCs to operate for a considerable period under part-load conditions.…”

Section: Introductionmentioning

confidence: 99%

Research on the Methods of Predicting Compressor Characteristic Curve

Hao,

Zhang,

Chi

et al. 2023

International Journal of Energy Research

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Compressors are one of the three major components of gas turbines, and their characteristic curves are used to analyze off-design performance. How to infer the characteristic curve based on different data is an important research topic. In this paper, PG9351FA gas turbine is taken as the research object. Two methods, artificial neural network and parameter estimation, are used to predict its characteristic curve, and the prediction accuracy and application conditions of the two methods are discussed. This article compares the two methods from the perspectives of known speed characteristic curve regression and unknown speed characteristic curve inference, analyzes the impact of sample size and sample error on their inference results, and quantitatively analyzes the error through statistical methods such as calculating the mean square deviation of the data. The application scope and conditions of different methods are provided. The research results show that the method based on neural network to infer the characteristic curve has high accuracy and is suitable for the prediction of known and unknown speed characteristic curves under sufficient data, but not for the prediction of unknown side curves. The elliptic equation fitting method based on parameter estimation has a slightly lower accuracy in processing the nearly vertical compressor characteristic curve, but it can be used as an effective and reliable method to infer the compressor characteristic curve in the case of a small amount of data. The modulization method based on parameter estimation has high accuracy and is applicable to the estimation of complete characteristic curve from partial data of known characteristic curve. In this paper, the application scope and conditions of these two methods are determined, which can provide reference for engineering practice.

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Classification and prediction of gas turbine gas path degradation based on deep neural networks

Cited by 9 publications

References 32 publications

Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

Predicting Steam Turbine Power Generation: A Comparison of Long Short-Term Memory and Willans Line Model

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Research on the Methods of Predicting Compressor Characteristic Curve

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