Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit
In response to the escalating challenges of rapid load fluctuations and intricate operating environments, supercritical power units demand enhanced control efficiency and adaptability. To this end, this study introduces a novel model predictive hybrid PID control strategy that integrates PID with mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6356 |
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| _version_ | 1850240881888591872 |
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| author | Qingfeng Yang Gang Chen Mengmeng Guo Tingting Chen Lei Luo Li Sun |
| author_facet | Qingfeng Yang Gang Chen Mengmeng Guo Tingting Chen Lei Luo Li Sun |
| author_sort | Qingfeng Yang |
| collection | DOAJ |
| description | In response to the escalating challenges of rapid load fluctuations and intricate operating environments, supercritical power units demand enhanced control efficiency and adaptability. To this end, this study introduces a novel model predictive hybrid PID control strategy that integrates PID with model predictive control (MPC), leveraging the operational characteristics of multi-loop systems. The proposed strategy adeptly marries the swift response of PID controllers with the foresight and optimization capabilities of MPC. A dynamic model of a supercritical unit is constructed using the subspace identification method. The model’s high precision is confirmed by its alignment with field data. Load change simulations demonstrate that the PID–MPC hybrid controller shows faster response times and more precise tracking capabilities compared to the feedforward-PID strategy. It achieves substantial improvements in the IAE index for three loops, with increases of 29.2%, 54.1%, and 57.3% over the feedforward-PID controller. An energy-saving performance analysis indicates that the proactive control actions of both the PID–MPC and MPC strategies lead to dynamic exergy efficiency and coal consumption rates with a broader range of dynamic process changes. The disturbance scenario simulation regarding the proposed controller achieves faster settling times and minimizes control deviation compared to the traditional controller. |
| format | Article |
| id | doaj-art-ee0fc714a3384d4ca482aa8e71aaaf8a |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-ee0fc714a3384d4ca482aa8e71aaaf8a2025-08-20T02:00:45ZengMDPI AGEnergies1996-10732024-12-011724635610.3390/en17246356Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical UnitQingfeng Yang0Gang Chen1Mengmeng Guo2Tingting Chen3Lei Luo4Li Sun5Dongfang Electric Qineng (Shenzhen) Technology Co., Ltd., Shenzhen 518000, ChinaState Key Laboratory of Low-Carbon Smart Coal-Fired Power Generation and Ultra-Clean Emission, Nanjing 210023, ChinaNational Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, ChinaDongfang Electric Qineng (Shenzhen) Technology Co., Ltd., Shenzhen 518000, ChinaDongfang Electric Qineng (Shenzhen) Technology Co., Ltd., Shenzhen 518000, ChinaNational Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, ChinaIn response to the escalating challenges of rapid load fluctuations and intricate operating environments, supercritical power units demand enhanced control efficiency and adaptability. To this end, this study introduces a novel model predictive hybrid PID control strategy that integrates PID with model predictive control (MPC), leveraging the operational characteristics of multi-loop systems. The proposed strategy adeptly marries the swift response of PID controllers with the foresight and optimization capabilities of MPC. A dynamic model of a supercritical unit is constructed using the subspace identification method. The model’s high precision is confirmed by its alignment with field data. Load change simulations demonstrate that the PID–MPC hybrid controller shows faster response times and more precise tracking capabilities compared to the feedforward-PID strategy. It achieves substantial improvements in the IAE index for three loops, with increases of 29.2%, 54.1%, and 57.3% over the feedforward-PID controller. An energy-saving performance analysis indicates that the proactive control actions of both the PID–MPC and MPC strategies lead to dynamic exergy efficiency and coal consumption rates with a broader range of dynamic process changes. The disturbance scenario simulation regarding the proposed controller achieves faster settling times and minimizes control deviation compared to the traditional controller.https://www.mdpi.com/1996-1073/17/24/6356supercritical unitcoordination controlmodel predictive controlenergy-saving analysisrapid load change |
| spellingShingle | Qingfeng Yang Gang Chen Mengmeng Guo Tingting Chen Lei Luo Li Sun Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit Energies supercritical unit coordination control model predictive control energy-saving analysis rapid load change |
| title | Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit |
| title_full | Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit |
| title_fullStr | Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit |
| title_full_unstemmed | Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit |
| title_short | Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit |
| title_sort | model predictive hybrid pid control and energy saving performance analysis of supercritical unit |
| topic | supercritical unit coordination control model predictive control energy-saving analysis rapid load change |
| url | https://www.mdpi.com/1996-1073/17/24/6356 |
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