Development and validation of a robust integrated thermal power plant model for load loss analysis and identification

The development of deep learning methodologies for the analysis of thermal power plant load losses requires a combination of real plant data and data derived from fundamental physics-based process models. For this purpose, a robust integrated power plant thermofluid process model of a complete +600M...

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Autores principales:	Marx Alton, Rousseau Pieter, Laubscher Ryno
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/023db1870dd440999706da85ee969594
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id	oai:doaj.org-article:023db1870dd440999706da85ee969594
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spelling	oai:doaj.org-article:023db1870dd440999706da85ee9695942021-12-02T17:13:35ZDevelopment and validation of a robust integrated thermal power plant model for load loss analysis and identification2261-236X10.1051/matecconf/202134700011https://doaj.org/article/023db1870dd440999706da85ee9695942021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/16/matecconf_sacam21_00011.pdfhttps://doaj.org/toc/2261-236XThe development of deep learning methodologies for the analysis of thermal power plant load losses requires a combination of real plant data and data derived from fundamental physics-based process models. For this purpose, a robust integrated power plant thermofluid process model of a complete +600MW coal-fired power plant was developed within the Flownex Simulation Environment. It consists of standard and compound components, combined with specially developed scripts to ensure complete energy balance, specifically on the two-phase tank components. This enables simulation of the complete plant operation to determine power output as a function of any given set of internal and external operational variables, boundary conditions and component states. The model was validated against real plant design and acceptance test data. In order to demonstrate the ability of the model it was used to evaluate the plant performance related to three specific load loss inducing scenarios. The results show that a combination of mechanical faults, process anomalies and operational phenomena can be analysed. This provides the basis for generating model-based performance data that can be combined with real plant data to facilitate the development of deep learning analytics tools for load loss fault diagnosis and root cause analysis, as well as fault propagation and load loss forecasting.Marx AltonRousseau PieterLaubscher RynoEDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 347, p 00011 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Marx Alton Rousseau Pieter Laubscher Ryno Development and validation of a robust integrated thermal power plant model for load loss analysis and identification
description	The development of deep learning methodologies for the analysis of thermal power plant load losses requires a combination of real plant data and data derived from fundamental physics-based process models. For this purpose, a robust integrated power plant thermofluid process model of a complete +600MW coal-fired power plant was developed within the Flownex Simulation Environment. It consists of standard and compound components, combined with specially developed scripts to ensure complete energy balance, specifically on the two-phase tank components. This enables simulation of the complete plant operation to determine power output as a function of any given set of internal and external operational variables, boundary conditions and component states. The model was validated against real plant design and acceptance test data. In order to demonstrate the ability of the model it was used to evaluate the plant performance related to three specific load loss inducing scenarios. The results show that a combination of mechanical faults, process anomalies and operational phenomena can be analysed. This provides the basis for generating model-based performance data that can be combined with real plant data to facilitate the development of deep learning analytics tools for load loss fault diagnosis and root cause analysis, as well as fault propagation and load loss forecasting.
format	article
author	Marx Alton Rousseau Pieter Laubscher Ryno
author_facet	Marx Alton Rousseau Pieter Laubscher Ryno
author_sort	Marx Alton
title	Development and validation of a robust integrated thermal power plant model for load loss analysis and identification
title_short	Development and validation of a robust integrated thermal power plant model for load loss analysis and identification
title_full	Development and validation of a robust integrated thermal power plant model for load loss analysis and identification
title_fullStr	Development and validation of a robust integrated thermal power plant model for load loss analysis and identification
title_full_unstemmed	Development and validation of a robust integrated thermal power plant model for load loss analysis and identification
title_sort	development and validation of a robust integrated thermal power plant model for load loss analysis and identification
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/023db1870dd440999706da85ee969594
work_keys_str_mv	AT marxalton developmentandvalidationofarobustintegratedthermalpowerplantmodelforloadlossanalysisandidentification AT rousseaupieter developmentandvalidationofarobustintegratedthermalpowerplantmodelforloadlossanalysisandidentification AT laubscherryno developmentandvalidationofarobustintegratedthermalpowerplantmodelforloadlossanalysisandidentification
_version_	1718381300688093184

Development and validation of a robust integrated thermal power plant model for load loss analysis and identification

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