Application of machine learning to predict the thermal power plant process condition

Sultanov, M. M.; Boldyrev, I. A.; Evseev, Kirill V.

doi:10.1088/1742-6596/2150/1/012029

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a power system, the thermal power plants serving as power suppliers are one of the most important links [1][2][3][4]. Their function is to complete the energy form transformation of fuel from chemical energy, to the thermal potential energy of steam, then to mechanical energy, and finally to electric energy.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Coupled Temperature Field in Multi-Modal Thermal Process and Its Analytical Solutions

Liu¹,

Liu²,

Wang³

2023

IJHT

View full text Add to dashboard Cite

To keep the main system and the auxiliary equipment of production system of thermal power plants operating normally, it's a necessary work to fully consider the periodical repeat features of thermal process when modelling the thermal process based on thermal state data of machine units. Since most equipment in power plant work in an electromagnetic coupling environment, a too high ambient temperature will affect the safe operation of units, so when studying the dynamic features of multi-modal thermal process of power plants and their soft environment, the temperature distribution features of multimodal thermal process should be classified. To solve these questions, this paper established a model for the coupled temperature field during multi-modal thermal process and studied its analytical solutions. At first, this paper proposed a multi-modal modelling method for solving the distribution features of coupled temperature during multi-modal thermal process, the method can quickly determine the optimal number of local models of multimodal thermal process of power plant units while reducing the computational load of the coupled temperature field model. Then, this paper introduced an improved niche method into the basic cross-entropy optimization algorithm and used it to solve the layout optimization problem of coupled temperature field during multi-modal thermal process. At last, the validity of the proposed method was verified by experimental results.

show abstract

Section: Introductionmentioning

confidence: 99%