Fuzzy modeling and stable model predictive tracking control of large-scale power plants |
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| Affiliation: | 1. Department of Energy Information and Automation, Southeast University, Nanjing 210096, China;2. Department of Electrical and Computer Engineering, Baylor University, One Bear Place #97356, Waco, TX 76798-7356, USA;1. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast Univerisity, Nanjing, 210096, China;2. Department of Chemical and Biological Engineering, Univerisity of Sheffield, Sheffield, S1 3JD, UK;3. Department of Electrical and Computer Engineering, Baylor University, One Bear Place #97356, Waco, TX, 76798-7356, USA;1. Key laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast Univerisity, Nanjing, 210096, China;2. Department of Chemical and Biological Engineering, Univerisity of Sheffield, Sheffield, S1 3JD, UK;3. Process Systems Enterprise Ltd, 26-28 Hammersmith Grove, London, W6 7HA, UK;4. Department of Electrical and Computer Engineering, Baylor University, One Bear Place #97356, Waco, TX, 76798-7356, USA |
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| Abstract: | This paper develops a stable model predictive tracking controller (SMPTC) for coordinated control of a large-scale power plant. First, a Takagi–Sugeno (TS) fuzzy model is established to approximate the behavior of the boiler–turbine coordinated control system (CCS) using fuzzy clustering and subspace identification (SID). Then, an SMPTC is designed based on the fuzzy model to track the power and pressure set-points while guaranteeing the input-to-state stability and the input constraints of the system. An output-based objective function is adopted for the proposed SMPTC so that the controller could be directly applicable for the data-driven model. Moreover, the effect of modeling mismatches and unknown plant variations has been overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an off-set free tracking performance can be achieved over a wide range load variation. |
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| Keywords: | Power plant Stable model predictive control Subspace identification Fuzzy clustering TS-fuzzy model |
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