Abstract
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).
Highlights
Aims and MotivationsGas turbines’ power share has increased progressively in the global power generation mix in later decades due to the progress in their design specifications, efficiency and reliability [1,2]
The maximum average mean squared error (MSE) was recorded for the MISO convolutional neural network (CNN) as 2.9210 × 10−4, and both networks worked successfully for more than 15 operating hours of the gas turbines (GTs); (3) It is newly shown that the NARX dynamic artificial neural network (ANN) was slightly superior in accuracy over the deep neural network, which indicates that the deep learning can be regarded as an alternative, but not substitutional, tool for the simulation of heavy-duty power GTs; in other words, they shall not replace the dynamic ANN, even with shallow architectures
Based on the most recent proposed future trends, simulated models of deep CNN and dynamic NARX ANN have been presented with extremely accurate results, which confirm the scientific merits of deep learning and shallow dynamic ANNs for the emulation of the GT power station performance
Summary
Gas turbines’ power share has increased progressively in the global power generation mix in later decades due to the progress in their design specifications, efficiency and reliability [1,2]. The combustion chamber is normally supplied by natural gas through the two valves–the pilot valve and the NG control (premix) valve–during low loads, startups or instances where there is a shortage in the NG, the combustion has to be stable so the pilot valve is supplied by fuel oil through a booster pump system. These operating modes are known by the operators as premix mode or premix/diffusion dual mode, in which the premix mode is active only in the normal NG operation from 50% to the rated power and the diffusion mode is possible in the entire load range (including startups and shutdowns). The scientific merit of this article will be discussed in the subsection, with a discussion of the related literature
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