Issue |
MATEC Web Conf.
Volume 347, 2021
12th South African Conference on Computational and Applied Mechanics (SACAM2020)
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Article Number | 00011 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/matecconf/202134700011 | |
Published online | 23 November 2021 |
Development and validation of a robust integrated thermal power plant model for load loss analysis and identification
Department of Mechanical Engineering, Applied Thermofluid Process Modelling Research Unit, University of Cape Town, Library Rd, Rondebosch, Cape Town, 7701, South Africa.
* Corresponding author E-mail address: altonmarx@gmail.com
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.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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