A neural network-based optimization approach for induction motor design

Idir, K.; Chang, Liuchen; Dai, H.

doi:10.1109/ccece.1996.548311

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…X i indicates the jth osprey, and x i,j indicates its jth dimension. Equation (37) states that the determined amounts for the objective function of the issue can be presented as a vector [58].…”

Section: Osprey Optimization Algorithm (Ooa)mentioning

confidence: 99%

“…ANNs have been used to solve numerous engineering issues [35,36]. Before being expanded to accommodate more polyphase rotating induction motors [37], a neural network was first utilized to produce a single-sided linear IM [38]. In these early research articles, the neural network transferred the input machine's geometrical design variables to the output machine's efficiency.…”

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confidence: 99%

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Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Gör

2024

Mathematics

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Industry is the primary application for induction machines. As such, it is essential to calculate the induction devices’ electrical properties accurately. With DC testing, no-load rotor tests, and locked rotor tests, one may empirically evaluate the electrical variables of induction motors. These tests are expensive and difficult to conduct, however. The information supplied by machine makers can also be used to accurately approximate the equivalent variables of the circuits in induction machines. This article has successfully predicted motor reactance (Xm) for both double- and single-cage models using artificial neural networks (ANN). Although ANNs have been investigated in the literature, the ANN structures were trained to use unmemorized training. Besides ANN, six other approaches have been suggested to address this issue: heap-based optimization (HBO), leagues championship algorithm (LCA), multi-verse optimization (MVO), osprey optimization algorithm (OOA), cuckoo optimization algorithm (COA), and sooty tern optimization algorithm (STOA). The efficaciousness of the suggested approaches was compared with each another. Regarding the obtained outcomes, the suggested MVO- multi-layer perceptron (MLP) technique performed better than the other five methods regarding reactance prediction, with R2 of 0.99598 and 0.9962, and RMSE of 20.31492 and 20.80626 in the testing and training phases, respectively. For the projected model, the suggested ANNs have produced great results. The novelty lies in the mentioned methods’ ability to tackle the complexities and challenges associated with induction motor reactance optimization, providing innovative approaches to finding optimal or near-optimal solutions. As researchers continue to explore and refine these techniques, their impact on motor design and efficiency will likely grow, driving advancements in electrical engineering.

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Section: Osprey Optimization Algorithm (Ooa)mentioning

confidence: 99%

mentioning

confidence: 99%

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Gör

2024

Mathematics

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“…This method also needs voluminous data for training neural networks. ANN can be trained to learn the relationship between input and output parameters of electrical machines [11]. Fuzzy logic is also being tried out for electrical machine design [12].…”

Section: Existence Of Constraintsmentioning

confidence: 99%

“…Approximate mean length of turns for power and control windings are given by Eqs. (11) and (12), respectively:…”

Section: Objective Functionmentioning

confidence: 99%

Optimal Design of Brushless Doubly Fed Reluctance Machine

Bhawalkar¹,

Narayan²,

Nerkar³

2018

Optimization and Control of Electrical Machines

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Optimization techniques are widely used in the design of electrical machines to obtain maximum performance at minimal capital cost. After a brief overview of some of the optimization techniques employed in electrical machine design, this chapter highlights the features of brushless doubly fed reluctance machine (BDFRM) and its optimal design. The simple and robust construction, variable speed operation, better performance compared to traditional counterpart, and requirement of partially rated converter for speed control have made BDFRM an attractive alternative for variable speed applications such as pumps, blower, and wind generators. Due to unusual construction of BDFRM, conventional design procedures cannot be applied. A few critical issues in the design of BDFRM that greatly affect its performance are discussed. Design optimization is performed using nonlinear programming technique for 6-4-2 pole reluctance rotor and 8-6-4 pole ducted rotor configurations of BDFRM. 2 kW prototypes are then constructed for laboratory use. The performance of the prototypes is examined through finite element analysis (FEA) employing Maxwell 16 software. The test results are also presented.

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Improved neural network model for induction motor design

Idir

Chang²,

Dai³

1998

IEEE Trans. Magn.

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An improved model of the artificial neural network for analysis and design of induction motors is presented. Parameters of the machine equivalent circuit are calculated using finite element method for a given motor geometry. The training of the neural network model is based on a decoupled system between geometrical variables and circuit parameters. This method efficiently improved the training and performance of the neural network model which can be used to predict machine performance and solve design optimization problems. Index terms-induction machine module, neural network, finite element method. H. C. J. de Jong, AC motor design with conventional and converter Haykin, Neural Networks, A Comprehensive Foundation. Macmillan

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A neural network-based optimization approach for induction motor design

Cited by 4 publications

References 19 publications

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Optimal Design of Brushless Doubly Fed Reluctance Machine

Improved neural network model for induction motor design

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