Abstract
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.
Highlights
With the current shift towards renewable energy, conventional thermal power plants are forced to respond with agility to the fluctuation in demand
Due to the sparseness of load loss classification data, this study proposes the development of an Integrated Power Plant Process Model (I3PM) for the controlled generation of load loss classification datasets
Flownex was selected for the development of the model based on its collective features and fundamental component-level analysis capabilities that are ideally suited for the development of power plant thermofluid components, as illustrated via studies performed by the Applied Thermofluid Process Modelling Research Unit [11]
Summary
With the current shift towards renewable energy, conventional thermal power plants are forced to respond with agility to the fluctuation in demand. Existing load loss classification systems are predominantly physics-based, utilising lumped zero-dimensional thermodynamic models These models are developed to evaluate each power plant system based on its current performance against the original design [2]. The objective is the development of a model that enables simulation of the complete power plant operation to determine power output as a function of any given set of internal and external operational variables, boundary conditions and component states. This will enable the generation of model-based load loss classification data that can be combined with real plant data for the training of deep neural networks. The integrated model development employs an operational plant mirroring framework based on the original plant design, combined with component level fundamental thermofluid physics models in the Flownex Simulation Environment [10]
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