Parameter identification of induction motor model using genetic algorithms

Alonge, F.; D’Ippolito, Filippo; Ferrante, G.; Raimondi, Francesco

doi:10.1049/ip-cta:19982408

Cited by 106 publications

(42 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have proposed solutions for torque error compensation with respect to temperature variation. Online parameter identification methods can be applied to maintain MTPA performance, but the methods are typically complicated to implement [13,14]. In [15], an algorithm using MTPA LUTs and a flux observer based on a voltage model was proposed, but the algorithm is expected to have a limited performance at low speed due to the voltage model-based flux observer used.…”

Section: Gopinath Style Stator Flux Linkage Observermentioning

confidence: 99%

A Torque Error Compensation Algorithm for Surface Mounted Permanent Magnet Synchronous Machines with Respect to Magnet Temperature Variations

Park

Lee

2018

Energies

View full text Add to dashboard Cite

This paper presents a torque error compensation algorithm for a surface mounted permanent magnet synchronous machine (SPMSM) through real time permanent magnet (PM) flux linkage estimation at various temperature conditions from medium to rated speed. As known, the PM flux linkage in SPMSMs varies with the thermal conditions. Since a maximum torque per ampere look up table, a control method used for copper loss minimization, is developed based on estimated PM flux linkage, variation of PM flux linkage results in undesired torque development of SPMSM drives. In this paper, PM flux linkage is estimated through a stator flux linkage observer and the torque error is compensated in real time using the estimated PM flux linkage. In this paper, the proposed torque error compensation algorithm is verified in simulation and experiment.

show abstract

Section: Gopinath Style Stator Flux Linkage Observermentioning

confidence: 99%

A Torque Error Compensation Algorithm for Surface Mounted Permanent Magnet Synchronous Machines with Respect to Magnet Temperature Variations

Park

Lee

2018

Energies

View full text Add to dashboard Cite

show abstract

“…A system identification problem using genetic algorithms is addressed in [9]. A similar problem of identification of parameters of an induction motor using Genetic Algorithms is developed in [10]. In a recent paper [11], genetic algorithm is used to estimate the parameters of a warranty cost model for a Malaysian automotive industry.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Parameters for Model Matching using Genetic Algorithms

Ps¹

2014

IJCA

View full text Add to dashboard Cite

This paper develops an optimization procedure to estimate the parameter values of a given dynamic system. The actual nonlinear model of the system is assumed available with the approximate range of parameter values. The model used in this work is the PS4 actuator with nozzle dynamics which is used for the liquid upper stage control system of PSLV. The objective is to estimate the parameter values so that the error between actual system output and the simulated output is minimized. This is achieved through Genetic Algorithms(GA) which is a global optimization technique. GAs are stochastic algorithms based on Darwin's theory of survival of the fittest. They are inspired by biological phenomena of natural genetics and natural selection. The basic elements of natural genetics-reproduction, crossover and mutation-are used in the genetic search procedures. GA is proved robust and efficient in finding optimal solutions in complex problem spaces.

show abstract

“…However, parameter sensitivity is one of the critical issues to be investigated because DB-DTFC algorithm is developed based on an inverse electrical machine model. The parameter sensitivity issue can be reduced by using online parameter identification methods but the methods are typically complicated to implement [9][10][11]. In [12,13], robustness evaluation of DB-DTFC is presented with respect to parameter variations for induction machine (IM) drives and IPMSM drives, respectively.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Deadbeat-Direct Torque and Flux Control for Permanent Magnet Synchronous Motor Drives with Respect to Parameter Variations

Lee

2018

Energies

View full text Add to dashboard Cite

This paper presents a stability analysis and dynamic characteristics investigation of deadbeat-direct torque and flux control (DB-DTFC) of interior permanent magnet synchronous motor (IPMSM) drives with respect to machine parameter variations. Since a DB-DTFC algorithm is developed based on a machine model and parameters, stability with respect to machine parameter variations should be evaluated. Among stability evaluation methods, an eigenvalue (EV) migration is used in this paper because both the stability and dynamic characteristics of a system can be investigated through EV migration. Since an IPMSM drive system is nonlinear, EV migration cannot be directly applied. Therefore, operating point models of DB-DTFC and CVC (current vector control) IPMSM drives are derived to obtain linearized models and to implement EV migration in this paper. Along with DB-DTFC, current vector control (CVC), one of the widely used control algorithms for motor drives, is applied and evaluated at the same operating conditions for performance comparison. For practical analysis, the US06 supplemental federal test procedure (SFTP), one of the dynamic automotive driving cycles, is transformed into torque and speed trajectories and the trajectories are used to investigate the EV migration of DB-DTFC and CVC IPMSM drives. In this paper, the stability and dynamic characteristics of DB-DTFC and CVC IPMSM drives are compared and evaluated through EV migrations with respect to machine parameter variations in simulation and experiment.

show abstract

Parameter identification of induction motor model using genetic algorithms

Cited by 106 publications

References 8 publications

A Torque Error Compensation Algorithm for Surface Mounted Permanent Magnet Synchronous Machines with Respect to Magnet Temperature Variations

A Torque Error Compensation Algorithm for Surface Mounted Permanent Magnet Synchronous Machines with Respect to Magnet Temperature Variations

Estimation of Parameters for Model Matching using Genetic Algorithms

Stability Analysis of Deadbeat-Direct Torque and Flux Control for Permanent Magnet Synchronous Motor Drives with Respect to Parameter Variations

Contact Info

Product

Resources

About