Neural Modelling of Steam Turbine Control Stage

Głuch, Jerzy; Drosińska-Komor, Marta

doi:10.1007/978-3-030-58964-6_9

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…In the above case [90], data from numerical degradation simulation were used for ANN learning purposes [91,92]. The time-consuming process of organizing ANN for diagnostic relations is the main disadvantage of this approach [93,94].…”

Section: Literature Gapmentioning

confidence: 99%

“…Further methods used in the literature (Table 1) include fuzzy logic [87], deterministic simulations based on measurements [89], support vector machine [91], extreme learning machine [92], and hierarchical fuzzy CMAC neural network [99], but they are extremely sensitive to the measurement data provided and do not have the kind of verification proposed in this paper, involving parallel revision of the process with a second approach. The methods above were mainly related to steam turbines.…”

Section: Literature Gapmentioning

confidence: 99%

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Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence

Ziółkowski,

Drosińska-Komor,

Głuch

et al. 2023

Energies

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This work is based on a literature review (191). It mainly refers to two diagnostic methods based on artificial intelligence. This review presents new possibilities for using genetic algorithms (GAs) for diagnostic purposes in power plants transitioning to cooperation with renewable energy sources (RESs). The genetic method is rarely used directly in the modeling of thermal-flow analysis. However, this assignment proves that the method can be successfully used for diagnostic purposes. The GA method was presented in this work for thermal-flow studies of steam turbines controlled from the central power system to obtain the stability of RESs. It should be remembered that the development of software using genetic algorithms to locate one-off degradations is necessary for a turbine that works sustainably with RESs. In this paper, against the background of the review, diagnostic procedures create an inverse model of a thermal power plant. Algorithms were used to detect fast global extremes through the convergence of simulated signatures with signs explaining degradation. In addition, statistical dependencies are used in the selection phase to accelerate fault detection. The created procedure allows obtaining a diagnosis in the form of a single degradation. This procedure turns out to be quite effective for the above example.

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Section: Literature Gapmentioning

confidence: 99%

Section: Literature Gapmentioning

confidence: 99%

Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence

Ziółkowski,

Drosińska-Komor,

Głuch

et al. 2023

Energies

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“…Authors of [20] presented the application of neural networks as an alternative approach for expensive calculations. Neural networks could be used in stage modeling when network is taught at similar geometry [21]. In some cases deep learning could be used with success when data is incomplete [22].…”

Section: Introductionmentioning

confidence: 99%

Archives of Thermodynamics

Głuch,

Ziółkowski,

Witanowski

et al. 2021

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Research regarding blade design and analysis of flow has been attracting interest for over a century. Meanwhile new concepts and design approaches were created and improved. Advancements in information technologies allowed to introduce computational fluid dynamics and computational flow mechanics. Currently a combination of mentioned methods is used for the design of turbine blades. These methods enabled us to improve flow efficiency and strength of turbine blades. This paper relates to a new type turbine which is in the phase of theoretical analysis, because the working fluid is a mixture of steam and gas generated in a wet combustion chamber. The main aim of this paper is to design and analyze the flow characteristics of the last stage of gas-steam turbine. When creating the spatial model, the atlas of profiles of reaction turbine steps was used. Results of computational fluid dynamics simulations of twisting of the last stage are presented. Blades geometry and the computational mesh are also presented. Velocity vectors, for selected dividing sections that the velocity along the pitch diameter varies greatly. The blade has the shape of its cross-section similar to action type blades near the root and to reaction type blades near the tip. Velocity fields and pressure fields show the flow characteristics of the last stage of gas-steam turbine. The net efficiency of the cycle is equal to 52.61%.

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