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Open AccessArticle

Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer

1
Key Lab of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China
2
Yunnan Electric Power Research Institute, CSG, Kunming 650000, China
3
School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(19), 8235; https://doi.org/10.3390/su12198235
Received: 18 August 2020 / Revised: 29 September 2020 / Accepted: 29 September 2020 / Published: 6 October 2020
(This article belongs to the Section Energy Sustainability)
Superheated steam temperature (SST) is one of the most critical parameters for the process safety, overall efficiency and pollution reduction of coal-fired power plants. However, SST control is challenging due to various disturbances and model uncertainties, especially in the face of the growing penetration of intermittent renewable energy into the power grid. To this end, a cascaded Disturbance Observer-PI (DOB-PI) control strategy is proposed to enhance control performance. The observer design and parameter tuning are carried out through mechanism analysis on the proposed structure. Furthermore, a robust loop shaping method is introduced as a hard constraint to balance the control performance and robustness. The controller parameters are optimized based on the multi-objective artificial bee colony optimization (MOABC) algorithm. Simulation results show that the proposed cascaded DOB-PI control strategy can significantly improve the disturbance rejection performance of both the inner- and outer-loops of the SST control system. This paper indicates promising prospects for the proposed method in future applications. View Full-Text
Keywords: superheated steam temperature; cascaded DOB-PI control; pareto optimization; disturbance rejection; coal-fired power plant superheated steam temperature; cascaded DOB-PI control; pareto optimization; disturbance rejection; coal-fired power plant
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MDPI and ACS Style

Hao, Y.-S.; Chen, Z.; Sun, L.; Liang, J.; Zhu, H. Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer. Sustainability 2020, 12, 8235. https://doi.org/10.3390/su12198235

AMA Style

Hao Y-S, Chen Z, Sun L, Liang J, Zhu H. Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer. Sustainability. 2020; 12(19):8235. https://doi.org/10.3390/su12198235

Chicago/Turabian Style

Hao, Yong-Sheng; Chen, Zhuo; Sun, Li; Liang, Junyu; Zhu, Hongxia. 2020. "Multi-Objective Intelligent Optimization of Superheated Steam Temperature Control Based on Cascaded Disturbance Observer" Sustainability 12, no. 19: 8235. https://doi.org/10.3390/su12198235

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