Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control

The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.

[1]  Kritchai Witheephanich,et al.  Explicit simplified MPC with an adjustment parameter adapted by a fuzzy system , 2019, J. Intell. Fuzzy Syst..

[2]  Alberto Bemporad,et al.  From linear to nonlinear MPC: bridging the gap via the real-time iteration , 2020, Int. J. Control.

[3]  P Antonio Carnero,et al.  Integration of Thermal Stress and Lifetime Supervision System of Steam Turbine Rotors , 2008 .

[4]  K.J. Astrom,et al.  Design of decoupled PID controllers for MIMO systems , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[5]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[6]  Tore Hägglund,et al.  The future of PID control , 2000 .

[7]  Amr Mokhtar,et al.  Advanced Boiler Control System for Steam Power Plants Using Modern Control Techniques , 2019, 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE).

[8]  Francesco Casella,et al.  Fast Start-up of a Combined-Cycle Power Plant: a Simulation Study with Modelica , 2006 .

[9]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[10]  Eduardo F. Camacho,et al.  Hybrid Nonlinear MPC of a Solar Cooling Plant , 2019, Energies.

[11]  Wojciech Radulski,et al.  Online Prediction of Temperature and Stress in Steam Turbine Components Using Neural Networks , 2015 .

[12]  Mariusz Banaszkiewicz,et al.  On-line monitoring and control of thermal stresses in steam turbine rotors , 2016 .

[13]  A. Arteconi,et al.  Fuzzy Logic Energy Management Strategy of a Multiple Latent Heat Thermal Storage in a Small-Scale Concentrated Solar Power Plant , 2020, Energies.

[14]  Michael Baldea,et al.  Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models , 2019, Comput. Chem. Eng..

[15]  Mario Innocenti,et al.  Design of a H ∞ Robust Controller with μ -Analysis for Steam Turbine Power Generation Applications , 2017 .

[16]  J. Richalet,et al.  Model predictive heuristic control: Applications to industrial processes , 1978, Autom..

[17]  Davide Tavernini,et al.  An Explicit Nonlinear Model Predictive ABS Controller for Electro-Hydraulic Braking Systems , 2020, IEEE Transactions on Industrial Electronics.

[18]  David W. Clarke,et al.  Generalized Predictive Control - Part II Extensions and interpretations , 1987, Autom..

[19]  J. L. Testud,et al.  Paper: Model predictive heuristic control , 1978 .

[20]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[21]  Lorenzo Cosi,et al.  Rotor Life Prediction and Improvement for Steam Turbines Under Cyclic Operation , 2011 .

[22]  M. Banaszkiewicz,et al.  A methodology for online rotor stress monitoring using equivalent Green’s function and steam temperature model , 2016 .

[23]  S. Morton,et al.  Model Predictive Control and the Optimization of Power Plant Load while Considering Lifetime Consumption , 2001, IEEE Power Engineering Review.

[24]  Matsumoto Hiroshi,et al.  Startup optimization of a combined cycle power plant based on cooperative fuzzy reasoning and a neural network , 1997 .

[25]  Yiguo Li,et al.  Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant , 2019 .

[26]  A M Boulos,et al.  A fuzzy logic approach to accommodate thermal stress and improve the start-up phase in combined cycle power plants , 2002 .

[27]  Wu Qian,et al.  Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions , 2020, Energies.

[28]  Valentina Colla,et al.  An adaptive Fuzzy logic-based approach to PID control of steam turbines in solar applications , 2017, Applied Energy.

[29]  Lorenz T. Biegler,et al.  Fast nonlinear model predictive control: Formulation and industrial process applications , 2013, Comput. Chem. Eng..

[30]  Damien Faille,et al.  Model Based Start-up Optimization of a Combined Cycle Power Plant , 2009 .

[31]  Cédric Join,et al.  Model-free control , 2013, Int. J. Control.

[32]  P. Gill,et al.  Sequential Quadratic Programming Methods , 2012 .

[33]  N. Munro,et al.  PID controllers: recent tuning methods and design to specification , 2002 .

[34]  Hendrik Kondziella,et al.  Flexibility requirements of renewable energy based electricity systems – a review of research results and methodologies , 2016 .

[35]  K. Åström,et al.  Revisiting the Ziegler-Nichols step response method for PID control , 2004 .

[36]  De-Wei Li,et al.  Model Predictive Control-Status and Challenges: Model Predictive Control-Status and Challenges , 2014 .

[37]  D. P. Atherton,et al.  Automatic tuning of optimum PID controllers , 1993 .

[38]  James B. Rawlings,et al.  Tutorial overview of model predictive control , 2000 .

[39]  Li-Sheng Hu,et al.  Online life estimation for steam turbine rotor , 2013 .

[40]  Grzegorz Nowak,et al.  Practical Algorithms for Online Thermal Stress Calculations and Heating Process Control , 2014 .

[41]  Caro Lucas,et al.  Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process , 2008, Int. J. Intell. Comput. Cybern..

[42]  Panagiotis D. Christofides,et al.  Real-Time Adaptive Machine-Learning-Based Predictive Control of Nonlinear Processes , 2020, Industrial & Engineering Chemistry Research.

[43]  Meng Li,et al.  Performance assessment for life extending control of steam turbine based on polynomial method , 2016 .

[44]  Tao Liu,et al.  U-model based predictive control for nonlinear processes with input delay , 2019, Journal of Process Control.

[45]  Sigurd Skogestad,et al.  Simple analytic rules for model reduction and PID controller tuning , 2003 .

[46]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[47]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[48]  Masao Arakawa,et al.  Intelligent multi-objective model predictive control applied to steam turbine start-up , 2018 .

[49]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[50]  Jay H. Lee,et al.  Model predictive control: Review of the three decades of development , 2011 .

[51]  Shinji Hayashi,et al.  Turbine start-up algorithm based on prediction of rotor thermal stress , 1995, SICE '95. Proceedings of the 34th SICE Annual Conference. International Session Papers.

[52]  Lars O. Nord,et al.  Optimal control of flexible natural gas combined cycles with stress monitoring: Linear vs nonlinear model predictive control , 2020 .

[53]  A. Propoi.  Use of linear programming methods for synthesizing sampled-data automatic systems , 1963 .

[54]  Brian Vad Mathiesen,et al.  Smart Energy Systems for coherent 100% renewable energy and transport solutions , 2015 .

[55]  Shih-Ping Han.  A globally convergent method for nonlinear programming , 1975 .

[56]  Tore Hägglund,et al.  Automatic tuning of simple regulators with specifications on phase and amplitude margins , 1984, Autom..

[57]  D. Q. Mayne,et al.  Suboptimal model predictive control (feasibility implies stability) , 1999, IEEE Trans. Autom. Control..

[58]  Panagiotis D. Christofides,et al.  Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation , 2019, AIChE Journal.