A Comparative Analysis of Two Neural-Network-Based Estimators for Efficiency Optimization of an Induction Motor Drive

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

doi:10.1109/ciep.2006.312164

Cited by 4 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, figuring out the parameters for each speed is a very difficult task. The idea of ANN-based parameter estimation, which calls for large computations and intricate processes [30]. An ANN-based reactive power-based model reference adaptive system (Q-MRAS) for improving induction motor drive stability has been presented [31].…”

Section: Figure 2 Artificial Neural Network Architecturesmentioning

confidence: 99%

A review on soft computing techniques used in induction motor drive application

Durgasukumar,

Prasad,

Gorantla

2024

IJPEDS

View full text Add to dashboard Cite

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>In this paper, hybrid models based on fuzzy systems and neural networks are reviewed. A fuzzy inference system is explicitly represented by expertise for induction motor drives, incorporating the learning capability of artificial neural networks. Researchers have been attracted to neuro-fuzzy techniques for training and inference in induction motor drives due to their efficiency. According to the classification of research articles from 2000 to 2020, this article presents a review of different artificial neural network techniques, fuzzy and neuro-fuzzy systems. The main objective is to provide a concise overview of current neuro-fuzzy research and to enable readers to identify appropriate methods according to their research interests.</p><p> </p></td></tr></tbody></table></div>

show abstract

Section: Figure 2 Artificial Neural Network Architecturesmentioning

confidence: 99%

A review on soft computing techniques used in induction motor drive application

Durgasukumar,

Prasad,

Gorantla

2024

IJPEDS

View full text Add to dashboard Cite

show abstract

“…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%

Investigating the Intelligent Methods of Loss Minimization in Induction Motors

et al. 2018

View full text Add to dashboard Cite

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.

show abstract