Induction Machine Parameter Range Constraints in Genetic Algorithm Based Efficiency Estimation Techniques

Bijan, Mahmud Ghasemi; Al-Badri, Maher; Pillay, Pragasen; Angers, Pierre

doi:10.1109/tia.2018.2836344

Cited by 24 publications

(13 citation statements)

References 20 publications

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“…Linear Induction Machines (LIMs) have many applications such as reciprocating compressor, packing materials handling and also it is used in subway systems in various countries such as the USA, Japan, and China [95]- [97]. Despite comprehensive research of MPC for conventional rotating machines presented in [95], [98], few works have focused on MPC for LIM applications. Different MPC strategies have been presented for LIM applications [98]- [106].…”

Section: Application Of Fcs-mpc In Linear Induction Machinementioning

confidence: 99%

Latest Advances of Model Predictive Control in Electrical Drives—Part II: Applications and Benchmarking With Classical Control Methods

Rodríguez

Garcia

Mora

et al. 2022

IEEE Trans. Power Electron.

152

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Section: Application Of Fcs-mpc In Linear Induction Machinementioning

confidence: 99%

Latest Advances of Model Predictive Control in Electrical Drives—Part II: Applications and Benchmarking With Classical Control Methods

Rodríguez

Garcia

Mora

et al. 2022

IEEE Trans. Power Electron.

152

View full text Add to dashboard Cite

show abstract

“…Finally, evaluate the solutions into the population and start again the procedure from the performance of competitive selection and repeat until some convergence criteria are satisfied. [16][17][18] The use of genetic algorithms is explained with the induction motor to improve its performance. The purpose of using the algorithms in the process synthetic load of the induction motors is to obtain transaction values for the varies cases "modulation index" for frequency modulation( , amplitude modulation( and mixed modulation and ( .Initially, the parameters name is changed to make it easier to use it to become as follows:…”

Section: Genetic Algorithms For Synthetic Loadingmentioning

confidence: 99%

Load Test of Induction Motors Based on PWM Technique using Genetic Algorithm

Elnaghi¹,

Mohammed²,

Dessouky³

et al. 2019

IJEM

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Genetic algorithms(GA) is optimization technique used in the equivalent load test of the induction motors to select the values of the factors(modulation indicators) that effect on the performance properties in terms of the values of the currents and the total loss within the machine. One way to choose these parameters is by trial and error while this paper based on GA method to improve the parameters selection. A model is designed to simulate the loading of the induction motor and obtain its own results by the MATLAB program 2017a. There are different methods used to achieve this task such as PWM inverter with different modulation techniques, Constant Voltage Variable Frequency (CVVF) method, Variable Voltage Constant Frequency (VVCF) method and Variable Voltage Variable Frequency (VVVF) method.

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“…No-load and locked rotor tests are not practicable in in situ [4,5]. In in situ situations, the Genetic Algorithm (GA) is mainly used for parameter identification, demanding more central processing unit (CPU) and computational time [6]. To sum up, the methods for efficiency estimation can be categorized as follows.…”

Section: Introductionmentioning

confidence: 99%

In-Situ Efficiency Estimation of Induction Motors Based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA)

Diarra

Yao

et al. 2022

Energies

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The accuracy estimation of induction motors’ efficiency is beneficial and crucial in the industry for energy savings. The requirement for in situ machine efficiency estimation techniques is increasing in importance because it is the precondition to making the energy-saving scheme. Currently, the torque and speed identification method is widely applied in online efficiency estimation for motor systems. However, the higher precision parameters, such as stator resistance Rs and equivalent resistance of iron losses Rfe, which are the key to the efficiency estimation process with the air gap torque method, are of cardinal importance in the estimation process. Moreover, the computation burden is also a severe problem for the real-time data process. To solve these problems, as for the torque and speed-identification-based efficiency estimation method, this paper presents a lower time burden method based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA). The contribution of the proposed method is to transform the disadvantages of former algorithms to develop a reliable hybrid algorithm to identify the crucial parameters, namely, Rs and Rfe. Sensorless speed identification based on the rotor slot harmonic frequency (RSHF) method is adopted for speed determination. This hybrid algorithm reduces the computation burden by about 1/3 compared to the classical genetic algorithm (GA). The proposed method was validated by testing a 5.5 kW motor in the laboratory and a 10 MW induction motor in the field.

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Induction Machine Parameter Range Constraints in Genetic Algorithm Based Efficiency Estimation Techniques

Cited by 24 publications

References 20 publications

Latest Advances of Model Predictive Control in Electrical Drives—Part II: Applications and Benchmarking With Classical Control Methods

Latest Advances of Model Predictive Control in Electrical Drives—Part II: Applications and Benchmarking With Classical Control Methods

Load Test of Induction Motors Based on PWM Technique using Genetic Algorithm

In-Situ Efficiency Estimation of Induction Motors Based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA)

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