Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization

Parameter estimation is an important part in the modeling of a hydro-turbine regulation system (HTRS), and the results determine the final accuracy of a model. A hydro-turbine is normally a non-minimum phase system with strong nonlinearity and time-varying parameters. For the parameter estimation of such a nonlinear system, heuristic algorithms are more advantageous than traditional mathematical methods. However, most heuristics based algorithms and their improved versions are not adaptive, which means that the appropriate parameters of an algorithm need to be manually found to keep the algorithm performing optimally in solving similar problems. To solve this problem, an adaptive fuzzy particle swarm optimization (AFPSO) algorithm that dynamically tunes the parameters according to model error is proposed and applied to the parameter estimation of the HTRS. The simulation studies show that the proposed AFPSO contributes to lower model error and higher identification accuracy compared with some traditional heuristic algorithms. Importantly, it avoids a possible deterioration in the performance of an algorithm caused by inappropriate parameter selection.

[1]  Xiaojun Wu,et al.  Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection , 2012, Evolutionary Computation.

[2]  W. Chang,et al.  PID controller design of nonlinear systems using an improved particle swarm optimization approach , 2010 .

[3]  Chuntian Cheng,et al.  Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling , 2017 .

[4]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[5]  Mohammad Mehdi Ebadzadeh,et al.  DNPSO: A Dynamic Niching Particle Swarm Optimizer for multi-modal optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[6]  Morteza Esfandyari,et al.  Adaptive fuzzy tuning of PID controllers , 2012, Neural Computing and Applications.

[7]  Xiaohui Yuan,et al.  Improved gravitational search algorithm for parameter identification of water turbine regulation system , 2014 .

[8]  Leike Zhang,et al.  A model establishment and numerical simulation of dynamic coupled hydraulic–mechanical–electric–structural system for hydropower station , 2017 .

[9]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[10]  Tedjani Mesbahi,et al.  Combined Optimal Sizing and Control of Li-Ion Battery/Supercapacitor Embedded Power Supply Using Hybrid Particle Swarm–Nelder–Mead Algorithm , 2017, IEEE Transactions on Sustainable Energy.

[11]  Edoardo Patelli,et al.  Model validation and stochastic stability of a hydro-turbine governing system under hydraulic excitations , 2018 .

[12]  Xinping Xiao,et al.  Multi- Swarm and Multi- Best particle swarm optimization algorithm , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[13]  Cheng-Chien Kuo,et al.  Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification , 2011, Appl. Math. Comput..

[14]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[15]  Yiling Lu,et al.  Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. , 2010, Cancer research.

[16]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[17]  Xin Yao,et al.  Cooperative Coevolutionary Algorithm-Based Model Predictive Control Guaranteeing Stability of Multirobot Formation , 2015, IEEE Transactions on Control Systems Technology.

[18]  Ke Wang,et al.  A PSO–GA optimal model to estimate primary energy demand of China , 2012 .

[19]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[20]  L. Coelho A quantum particle swarm optimizer with chaotic mutation operator , 2008 .

[21]  R. P. Saini,et al.  A review on hydropower plant models and control , 2007 .

[22]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[23]  P. Regulski,et al.  Estimation of Composite Load Model Parameters Using an Improved Particle Swarm Optimization Method , 2015, IEEE Transactions on Power Delivery.

[24]  Mark Hoogendoorn,et al.  Parameter Control in Evolutionary Algorithms: Trends and Challenges , 2015, IEEE Transactions on Evolutionary Computation.

[25]  A. R. Jordehi Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules , 2018 .

[26]  Ponnuthurai N. Suganthan,et al.  A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[27]  Lennart Ljung,et al.  Kernel methods in system identification, machine learning and function estimation: A survey , 2014, Autom..

[28]  A. H. Elsheikh,et al.  Review on applications of particle swarm optimization in solar energy systems , 2018, International Journal of Environmental Science and Technology.

[29]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[30]  Mingjiang Wang,et al.  Nonlinear modeling and dynamic control of hydro-turbine governing system with upstream surge tank and sloping ceiling tailrace tunnel , 2016 .

[31]  Ke Wang,et al.  A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction , 2015 .

[32]  Jianzhong Zhou,et al.  Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm , 2011 .

[33]  Q. Niu,et al.  A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells , 2014 .

[34]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[35]  Prudence W. H. Wong,et al.  Parameter estimation of photovoltaic model via parallel particle swarm optimization algorithm , 2016 .

[36]  Fuqing Zhao,et al.  A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem , 2019, Expert Syst. Appl..

[37]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[38]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[39]  Xiaodong Li,et al.  Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[40]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[41]  Athanasios V. Vasilakos,et al.  Vector coevolving particle swarm optimization algorithm , 2017, Inf. Sci..

[42]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[43]  Chu Zhang,et al.  A parameter adaptive identification method for a pumped storage hydro unit regulation system model using an improved gravitational search algorithm , 2017, Simul..

[44]  Anis Sakly,et al.  Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction , 2018, Energy Conversion and Management.

[45]  Yuanchu Cheng,et al.  Research on Francis Turbine Modeling for Large Disturbance Hydropower Station Transient Process Simulation , 2015 .

[46]  A. Correcher,et al.  Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms , 2018, Energies.

[47]  A. Rezaee Jordehi,et al.  Binary particle swarm optimisation with quadratic transfer function: A new binary optimisation algorithm for optimal scheduling of appliances in smart homes , 2019, Appl. Soft Comput..

[48]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[49]  Long Chen,et al.  Basic Modeling and Simulation Tool for Analysis of Hydraulic Transients in Hydroelectric Power Plants , 2008, IEEE Transactions on Energy Conversion.

[50]  Rajesh Kumar,et al.  A review on particle swarm optimization algorithms and their applications to data clustering , 2011, Artificial Intelligence Review.

[51]  A. E. Eiben,et al.  From evolutionary computation to the evolution of things , 2015, Nature.

[52]  Yu Huang,et al.  Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm , 2015, PloS one.

[53]  Amir Hossein Gandomi,et al.  Multi-stage genetic programming: A new strategy to nonlinear system modeling , 2011, Inf. Sci..