Abstract
The ultra-supercritical (USC) coal-fired boiler-turbine unit has been widely used in modern power plants due to its high efficiency and low emissions. Since it is a typical multivariable system with large inertia, severe nonlinearity, and strong coupling, building an accurate model of the system using traditional identification methods are almost impossible. In this paper, a deep neural network framework using stacked auto-encoders (SAEs) is presented as an effective way to model the USC unit. In the training process of SAE, maximum correntropy is chosen as the loss function, since it can effectively alleviate the influence of the outliers existing in USC unit data. The SAE model is trained and validated using the real-time measurement data generated in the USC unit, and then compared with the traditional multilayer perceptron network. The results show that SAE has superiority both in forecasting the dynamic behavior as well as eliminating the influence of outliers. Therefore, it can be applicable for the simulation analysis of a 1000 MW USC unit.
Highlights
With the fast development of China’s economy in the 21st century, the demand for electricity is growing rapidly
It is found that the performance of the multi-layer perceptron (MLP) network is lower than that of the stacked auto-encoders (SAEs) network, as it is a shallow architecture which often suffers from uncontrolled convergence speed and local optimality, especially when the training sample size grows too large
For the modeling of a 1000 MW USC coal-fired boiler-turbine unit, a deep neural network (DNN) framework using an SAE was proposed in this paper
Summary
With the fast development of China’s economy in the 21st century, the demand for electricity is growing rapidly. In [6], the dynamic model of a 1000 MW power plant was established by combining the experimental modeling approach and the first-principle modeling approach, which can be feasible and applicable for simulation analysis and testing control algorithms Based on this model, a sliding mode predictive controller was proposed in [19] to achieve excellent load tracking ability under wide-range operation. The supervisory information system, which provides comprehensive optimization for the plant’s real-time production, collecting all the process data and storing the data in the historical database These massive datasets are of great value since they can reflect the actual operational condition of the USC unit and embody the unit’s complex physical and chemical characteristics. In order to establish an accurate USC unit model using generated big data, SAE is adopted as the DNN model structure in this paper.
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