A Neural Network Based Optimal Rotor Flux Estimator for Efficiency Optimization of an Induction Motor Drive

Mino‐Aguilar, Gerardo; Moreno-Eguilaz, J.M.; Pryymak, Bogdan; Peracaula, J.

doi:10.1109/isie.2006.295970

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…as can be seen, network has 2 input and 1 output. [40,44] References [45]- [46] have used an ANN with 4 inputs and one output. The four inputs are speed, torque, resistance difference ΔRr and inductance differences ΔLm, and an optimal flux is estimated at the output.…”

Section: The Artificial Neural Networkmentioning

confidence: 99%

“…The ANN structure used in these references is illustrated in Figure 28. [45][46] In [47], a combination of PSO and ANN is used to estimate d-axis flux. In this approach, the instructed ANN with PSO receives speed and load torque values and then applies the slip frequency in the optimal objective function conditions (best fitness) to the motor.…”

Section: The Artificial Neural Networkmentioning

confidence: 99%

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Investigating the Intelligent Methods of Loss Minimization in Induction Motors

et al. 2018

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Induction motors are widely used in industry. Given the increasing demand for electric machines in different industries, optimization of these machines to achieve a high efficiency with low cost is of utmost importance. Loss-minimization in motor is done in three ways: 1) optimizing motor selection and design; 2) improving motor power supply waveforms; and 3) using appropriate controlling methods in drives. Often, inductive motors provide the maximum efficiency in their nominal load. In most applications it is necessary for a motor to work in light loads for a long time, e.g. in conveyors, elevators, etc. In these conditions, the machine load is not the nominal load, and a higher percentage of the input power is lost. So, in the case of variable load, the first and second methods cannot increase the efficiency; but the third method provides a large flexibility in decreasing motor losses. In this paper, the application of the third method in lossminimization is reviewed. These motor losses are mostly related to the controlling strategy and basically occur in light-load conditions. There are various strategies to decrease this kind of losses, which are generally divided into two categories: classic methods and intelligent methods. In this paper, first the classic methods, including losses model control (LMC), flux control as a function of torque and search control (SC), are discussed. Then the intelligent methods, such as genetic algorithm, PSO, fuzzy logic and artificial neural network are investigated. This paper is presented while the last methods of efficiency improvement are being investigated and each method is described briefly.

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Section: The Artificial Neural Networkmentioning

confidence: 99%

Section: The Artificial Neural Networkmentioning

confidence: 99%

Investigating the Intelligent Methods of Loss Minimization in Induction Motors

et al. 2018

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“…Only 16 examples are used as training data. In [14] the training set is composed of 3100 examples. The ANN has 4 neurons in the input layer, 5 neurons in the first hidden layer and output the reference rotor flux.…”

Section: The Artificial Neural Network Usedmentioning

confidence: 99%

“…No experimental results are presented. A more complete scheme is presented in [14], in which the magnetizing inductance and rotor resistance are estimated and used together with measured speed and estimated electromagnetic torque as inputs of the ANN that determines the flux for maximum efficiency of the drive. However, the rotor flux is estimated using the induction machine stator equation (voltage model), which tends to become unstable in low speed operation.…”

Section: Introductionmentioning

confidence: 99%

Application Of Artificial Neural Network In The Efficient Control Of Three-phase Induction Motor

Santos¹,

Neves²,

Aquino³

et al. 2010

Eletrônica de Potência

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This work presents a method for increasing the efficiency of three-phase induction motor drives over the entire operation range. The direct field oriented control of induction motors, including the effects of magnetic saturation is used. The magnetic saturation effect in the machine is modeled by the non-linear magnetization curve of the iron core. Artificial neural networks are used to predict the optimum reference rotor flux to be used in the vector control. Details about the chosen neural networks are given. Simulation and experimental results are presented and the motor losses reduction during different load conditions is evaluated.

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“…In [8] the training set is composed of 3100 examples. The ANN has 4 neurons in the input layer, 5 neurons in the first hidden layer and output the reference rotor flux.…”

Section: The Artificial Neural Network Usedmentioning

confidence: 99%

Application of artificial neural network in the efficient control of three-phase induction motor

Santos

Neves

Aquino

et al. 2009

2009 Brazilian Power Electronics Conference

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Resumo-This work presents a method for increasing the efficiency of three-phase induction motor drives over the entire operation range. The direct field oriented control of induction motors, including the effects of magnetic saturation is used. The magnetic saturation effect in the machine is modeled by the non-linear magnetization curve of the iron core. Artificial neural networks are used to predict the optimum reference rotor flux to be used in vector control. Details about the chosen neural networks are given. Simulation and experimental results are presented and the motor losses reduction during different load conditions is evaluated.

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A Neural Network Based Optimal Rotor Flux Estimator for Efficiency Optimization of an Induction Motor Drive

Cited by 5 publications

References 18 publications

Investigating the Intelligent Methods of Loss Minimization in Induction Motors

Investigating the Intelligent Methods of Loss Minimization in Induction Motors

Application Of Artificial Neural Network In The Efficient Control Of Three-phase Induction Motor

Application of artificial neural network in the efficient control of three-phase induction motor

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