Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm

This study investigates the application of a multiobjective genetic algorithm for the obtaining of a set of weighting factors suitable for use in the model predictive torque control (MPTC) of an induction motor variable speed drive. The MPTC approach aims at minimizing a cost function at each step and has been highlighted for its fast torque response, easy incorporation of system constraints, and absence of voltage modulators. Nevertheless, its structure contains weighting factors in the cost function, which lacks an analytical design procedure. The nondominated sorting genetic algorithm II (NSGA-II) was designed for a tradeoff between torque and flux performances and average switching frequency of the system. Experimental results showed NSGA-II offered a Pareto set of feasible solutions, so that torque ripple, flux ripple, or average switching frequency can be minimized, depending on the solution chosen. Its application constitutes a project tool for MPTC weighting factors that adjusts several factors concomitantly and incorporates desired restrictions.

[1]  Bart De Schutter,et al.  Weight optimisation for iterative distributed model predictive control applied to power networks , 2013, Eng. Appl. Artif. Intell..

[2]  C.W. Brice,et al.  Estimation of induction motor parameters by a genetic algorithm , 2003, Conference Record of the 2003 Annual Pulp and Paper Industry Technical Conference, 2003..

[3]  Joachim Bocker,et al.  Self-optimizing Model Predictive Direct Torque Control for electrical drives , 2015, 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE).

[4]  S. Alireza Davari.  Predictive Direct Angle Control of Induction Motor , 2016, IEEE Transactions on Industrial Electronics.

[5]  R. Kennel,et al.  Predictive control of inverter supplied electrical drives , 2000, 2000 IEEE 31st Annual Power Electronics Specialists Conference. Conference Proceedings (Cat. No.00CH37018).

[6]  Saad Mekhilef,et al.  Experimental validation of minimum cost function-based model predictive converter control with efficient reference tracking , 2015 .

[7]  Ralph Kennel,et al.  Encoderless Finite-State Predictive Torque Control for Induction Machine With a Compensated MRAS , 2014, IEEE Transactions on Industrial Informatics.

[8]  Wei Xu,et al.  An Improved Direct Torque Control for Three-Level Inverter-Fed Induction Motor Sensorless Drive , 2012, IEEE Transactions on Power Electronics.

[9]  Hamid Mahmoudi,et al.  Fuzzy adjustment of weighting factor in model predictive control of permanent magnet synchronous machines using current membership functions , 2016, 2016 IEEE Power and Energy Conference at Illinois (PECI).

[10]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Yongchang Zhang,et al.  Two-Vector-Based Model Predictive Torque Control Without Weighting Factors for Induction Motor Drives , 2016, IEEE Transactions on Power Electronics.

[13]  Manfred Morari,et al.  Model Predictive Direct Torque Control—Part I: Concept, Algorithm, and Analysis , 2009, IEEE Transactions on Industrial Electronics.

[14]  Leopoldo G. Franquelo,et al.  Guidelines for weighting factors design in Model Predictive Control of power converters and drives , 2009, 2009 IEEE International Conference on Industrial Technology.

[15]  Mehdi Salehifar,et al.  Simplified fault tolerant finite control set model predictive control of a five-phase inverter supplying BLDC motor in electric vehicle drive , 2016 .

[16]  P. Vas.  Vector control of AC machines , 1990 .

[17]  Y. Censor.  Pareto optimality in multiobjective problems , 1977 .

[18]  Navid Razmjooy,et al.  A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System , 2016 .

[19]  Xavier Blasco,et al.  Generalized predictive control using genetic algorithms (GAGPC) , 1998 .

[20]  Saad Mekhilef,et al.  Modified Model Predictive Control of a Bidirectional AC–DC Converter Based on Lyapunov Function for Energy Storage Systems , 2016, IEEE Transactions on Industrial Electronics.

[21]  Rohit Saxena,et al.  A comparative study of the various genetic approaches to solve multi-objective optimization problems , 2014, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).

[22]  C. Gerada,et al.  Induction Motor parameters identification using Genetic Algorithms for varying flux levels , 2008, 2008 13th International Power Electronics and Motion Control Conference.

[23]  Saad Mekhilef,et al.  Model predictive control of induction motor with delay time compensation: An experimental assessment , 2015, 2015 IEEE Applied Power Electronics Conference and Exposition (APEC).

[24]  U. Ammann,et al.  Model Predictive Control—A Simple and Powerful Method to Control Power Converters , 2009, IEEE Transactions on Industrial Electronics.

[25]  Wei Xie,et al.  Model-Based Predictive Direct Control Strategies for Electrical Drives: An Experimental Evaluation of PTC and PCC Methods , 2015, IEEE Transactions on Industrial Informatics.

[26]  Leonardo Trujillo,et al.  Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm , 2015, Appl. Soft Comput..

[27]  José R. Espinoza,et al.  Predictive Torque and Flux Control Without Weighting Factors , 2013, IEEE Transactions on Industrial Electronics.

[28]  Samir Kouro,et al.  Multiobjective Fuzzy Predictive Torque Control of an induction machine fed by a 3L-NPC inverter , 2015, 2015 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE).

[29]  Pericle Zanchetta,et al.  Heuristic multi-objective optimization for cost function weights selection in finite states model predictive control , 2011, 2011 Workshop on Predictive Control of Electrical Drives and Power Electronics.

[30]  Ralph Kennel,et al.  High-Performance Control Strategies for Electrical Drives: An Experimental Assessment , 2012, IEEE Transactions on Industrial Electronics.

[31]  I. Takahashi,et al.  High-performance direct torque control of an induction motor , 1989 .

[32]  Te-Jen Su,et al.  Torque ripple reduction of induction motor based on a hybrid method of model predictive torque control and particle swarm optimization , 2016 .

[33]  Lihui Wang,et al.  Multi-objective Evolutionary Optimisation for Product Design and Manufacturing , 2011 .

[34]  Yongchang Zhang,et al.  Model-Predictive Flux Control of Induction Motor Drives With Switching Instant Optimization , 2015, IEEE Transactions on Energy Conversion.

[35]  Saad Mekhilef,et al.  Stability and Performance Investigations of Model Predictive Controlled Active-Front-End (AFE) Rectifiers for Energy Storage Systems , 2015 .

[36]  Saad Mekhilef,et al.  Imposed Weighting Factor Optimization Method for Torque Ripple Reduction of IM Fed by Indirect Matrix Converter with Predictive Control Algorithm , 2015 .

[37]  R. Kennel,et al.  An Improved FCS–MPC Algorithm for an Induction Motor With an Imposed Optimized Weighting Factor , 2012, IEEE Transactions on Power Electronics.

[38]  Saad Mekhilef,et al.  Model Predictive Torque Ripple Reduction with Weighting Factor Optimization Fed by an Indirect Matrix Converter , 2014 .

[39]  Marco Rivera,et al.  Model Predictive Control for Power Converters and Drives: Advances and Trends , 2017, IEEE Transactions on Industrial Electronics.

[40]  Marian P. Kazmierkowski,et al.  State of the Art of Finite Control Set Model Predictive Control in Power Electronics , 2013, IEEE Transactions on Industrial Informatics.