Oxidation Behaviour of Ni-Base Superalloys in Supercritical Water: A Review

Main Steam Temperature Control Based on Variable Universe Fuzzy Dynamic Matrix Control

In this article, a digital twin-based subspace model predictive control scheme is proposed for solving model mismatch caused by parameter perturbations of thermal power plants. First, the rational range of essential parameters in the nonlinear model is identified based on the nominal data of an actual 600 MW power plant, which provides necessary foundation for subsequent studies. Second, to improve the load-changing capacity under parameter disturbances, a novel digital twin-based subspace model predictive control algorithm is established by regarding the crucial parameter as extended input of predictive model. Finally, the small fluctuations during steady-state operation are effectively restrained through transforming weights and constraints slowly and smoothly at the end of load change. Simulation results illustrate that compared with the conventional subspace-based model predictive control, the proposed digital twin-based subspace model predictive control scheme can obtain a better performance in terms of both dynamic regulation rate and steady-state performance under parameter perturbations.

Digital twin-based subspace model predictive control for thermal power plant

This document provides the motivations and a brief introduction to the Special Issue entitled “Advanced Control Design and Fault Diagnosis”, which aims at presenting several solutions to the advanced control design and fault diagnosis systems. These methodologies can be considered in the general framework of advanced control, fault diagnosis and fault tolerant control systems, which are also able to improve the safety of the system under monitoring. The focuses of the current research in this field addressed in this Special Issue are also presented with emphasis on the practical application to simulated and realistic examples, which should provide an overall picture of current and future developments in this area. The works of this Special Issue represent suitably extended contributions selected by the proponents from the ACD2019—the 15th European Workshop on Advanced Control and Diagnosis, which was organised in Bologna, Italy on 21st–22nd November.

Advanced Control Design and Fault Diagnosis

Regulating performance of the main steam temperature (MST) system concerns the economy and safety of the coal-fired power plant (CFPP). This paper develops an offset-free offline robust model predictive control (RMPC) strategy for the MST system of CFPP. Zonotope-type uncertain model is utilized as the prediction model in the proposed RMPC design owing to its features of higher accuracy, compactness of representation and less complexity. An offline RMPC aiming at the system robustness and computational efficiency is then developed to maintain the desired steam temperature in case of wide operating condition change. The proposed RMPC is realized by two stages: in the first stage, the RMPC law set, which is the piecewise affine (PWA) of the MST system state is designed offline; then in the second stage, the explicit control law is selected online according to the current state. To achieve an offset-free tracking performance, a manipulated variable target observer is employed to update the chosen RMPC law. The control simulations using on-site operating data of a 1000 MW ultra-supercritical power plant show that the proposed approach can achieve satisfactory control performance and online computation efficiency even under complicated operating conditions.

https://www.mdpi.com/1996-1073/13/15/3775/pdf

An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant

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An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant