Artificial intelligence-based rotor position estimation for a 6/4 pole switched reluctance machine from phase inductance

Susitra, D.; Paramasivam, S.

doi:10.1504/ijmic.2014.063877

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Both the benefits are not satisfied in any of these models. Few of the recent research publications have reported using the Adaptive Neuro fuzzy inference system (ANFIS) techniques for the computation of magnetic parameters [23][24][25] and estimation of rotor position [26]. On the overview of the publications on modeling of SRM, it is observed that none of the papers have paid attention in using Multivariate nonlinear regression technique (MVNLRT) for estimating its nonlinear inductance model.…”

Section: Introductionmentioning

confidence: 99%

MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

Dhanarajalu¹

2021

Emerging Electric Machines - Advances, Perspectives and Applications

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This chapter aims in presenting the methods for the accurate estimation of highly non linear phase inductance profile of a switched reluctance motor (SRM). The magnetization characteristics of a test SRM is derived from the SRDaS (Switched Reluctance Design and Simulation) simulation software. Statistical interpolation based regression analysis and Artificial Intelligence (AI) techniques are used for developing the computationally efficient inductance model. Multi Variate Non linear Regression (MVNLR) from the class of regression analysis and Adaptive Neuro Fuzzy Inference System (ANFIS) under the class of AI are implemented and tested on the simulated data. Non linear Inductance profile L(I,θ) of SRM is successfully estimated for the complete working range of phase currents (Iph). At each Iph, L(I,θ) values are estimated and presented for one cycle of rotor position (θ). Estimated inductance profile based on the two proposed methods is observed to be in excellent correlation with the true value of data.

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Section: Introductionmentioning

confidence: 99%

MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

Dhanarajalu¹

2021

Emerging Electric Machines - Advances, Perspectives and Applications

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“…The new techniques of control, estimation and monitoring of several electromechanical systems have widely used the artificial intelligence based on fuzzy logic system, artificial neural network or combined structure techniques (Susitra and Paramasivam, 2014;Suganthi et al, 2015;Nyanteh et al, 2013;Toshio, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy monitoring of stator and rotor winding faults for DFIG used in wind energy conversion system

Merabet

Bahi

2017

IJMIC

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According to its high robustness, the use of doubly-fed induction generators in the wind energy conversion takes an important place in the world of production of electrical energy. This type of conversion became very attractive because of its manufacturing environments, low cost and operation with an easily available power supply. The increase interest in wind energy conversion has been accompanied by efforts to improve reliability, effective condition monitoring and better efficiency. In this work, a new technique is proposed for monitoring and detection of inter-turn short-circuit ITSC and open phase circuits in the stator or rotor windings of wind turbines based on doubly-fed induction generator The principle of the suggested technique is based on the acquisition of the stator and the rotor currents of a doubly-fed induction generator with the aim to calculate the values of root mean square amplitude, in addition to the knowledge expressed in rules and membership function. This technique is verified using simulations performed via the model of doubly-fed induction generators built in MATLAB ® Simulink. Bahi Tahar received his Engineering, Magister and Doctorate degrees in Electrical Engineering from the Badji Mokhtar University, Annaba, Algeria in 1983, 1986 and 2006, respectively. Since 1983, he has been with the Department of Electrical of the University of Annaba, Algeria where he is currently a Professor of Electrical Engineering. His main research fields include the control of electrical machines, power electronic applications and renewable energy. This paper is a revised and expanded version of a paper entitled 'Monitoring and detection of the electromagnetic fault in a wind turbine based in DFIG using fuzzy logic approach' presented at Keywords

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“…Neural networks have been successfully applied for capturing associations or discovering T regularities within a set of patterns where the volume, number of variables or diversity of the data is very great (Susitra and Paramasivam, 2014). They also work well in revealing interrelationships which are vaguely understood or difficult to describe adequately with conventional approaches.…”

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confidence: 99%

Structure optimisation of input layer for feed-forward NARX neural network

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2016

IJMIC

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Artificial intelligence-based rotor position estimation for a 6/4 pole switched reluctance machine from phase inductance

Cited by 4 publications

References 13 publications

MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

MNLR and ANFIS Based Inductance Profile Estimation for Switched Reluctance Motor

Fuzzy monitoring of stator and rotor winding faults for DFIG used in wind energy conversion system

Structure optimisation of input layer for feed-forward NARX neural network

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