An Interval Type-2 Fuzzy Based DTC of IMD Using the Hybrid Duty Ratio Control

N, Venkataramana Naik

doi:10.1109/peds44367.2019.8998873

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…SVM has a strong theoretical basis, which is an important aspect of its superior to other algorithms. It is the support of these theories that SVM overcomes some shortcomings that neural networks cannot overcome [12,13]. The classification algorithm, regression algorithm, and multiclassification algorithm of SVM are the basis of the algorithm proposed.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Zhou

2022

Mobile Information Systems

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With the development of financial globalization and financial market volatility, credit risk has become more prominent and serious, and how to establish an effective enterprise credit evaluation system and bank credit risk evaluation model, provide scientific quantitative decision-making basis for bank decision-making, reduce non-performing loans, and improve the quality of credit assets is a common research topic faced by domestic banks. At present, domestic banks have not been effective to establish risk prevention as the core of credit culture and long-term mechanism, the existence of nonperforming loans is still not fully resolved, new risks continue to appear, and there is a lack of a perfect and effective credit risk evaluation system. With the development of the Internet and financial institutions and the fusion, banks and financial institutions drastically increase the recorded data, and this provides a good prerequisite for the application of intelligent algorithms. In view of the shortcomings of BP neural network in the establishment of credit risk assessment model, such as poor promotion ability and long prediction time, and considering that support vector machine (SVM) can deal with some multi-classification problems, this paper introduces SVM method into the field of bank credit risk assessment and establishes an optimization model of credit risk assessment. This paper discusses the structure and algorithm principle of SVM classification method and proposes an integrated SVM based on fuzzy integral to solve this kind of problem. The results show that the algorithm can effectively improve the prediction accuracy, solve the problem of high computation cost, reduce the occupied memory space, improve the operation efficiency, shorten the training time, and provide a more reliable basis for the rapid and effective evaluation of bank credit risk. On the one hand, the research results expand the application of artificial intelligence technology in the field of economic research; the evaluation model can continuously and accurately measure credit risk is obtained, which provides the necessary basis for upgrading and optimizing credit decision-making, so it has high theoretical value and practical value.

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Section: Mobile Information Systemsmentioning

confidence: 99%

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Zhou

2022

Mobile Information Systems

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“…If the reference vector ( V* ) is in sector 1, then according to the volt‐second balance equation, the active/zero vector timings ( t 1/2 /t z ) over a sampling time ( t s ) and the corresponding duty ratios are expressed as 9,28

δ_{1} = t_{1} / t_{s} = \sqrt{3} \frac{V^{*}}{V_{italicdc}} \sin ((), 60 ° - γ) = 1.155 M \sin ((), 60 ° - γ),

δ_{2} = t_{2} / t_{s} = \sqrt{3} \frac{V^{*}}{V_{italicdc}} \sin γ = 1.155 M \sin γ,

δ_{0} = t_{z} / t_{s} = 1 - ((), δ_{1} + δ_{2}) = 1 - 1.155 M ((), \sin ((), 60 ° - γ) - \sin γ),

where γ is the angle made reference vector w.r.t V 1 vector and M is modulation index. The stator flux ripple (SFR) of a three‐phase IM is calculated as given as follows:…”

Section: Duty Ratio Control For Csv and Cseqmentioning

confidence: 99%

An improved performance ofPV‐fedIVCinduction motor drive using clamping sequence duty ratio control

Narendra

Panda

et al. 2021

Int Trans Electr Energ Syst

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Summary This article discloses a clamping sequence duty ratio control (CSEQ‐DRC) for photovoltaic (PV)‐fed indirect vector control (IVC) of induction motor (IM) drive. Solar PV is one of the prominent renewable green energy resources and made a pollution‐free power generation without carbon emission. The PV‐fed IM drives are used in many applications such as water pumping, electric vehicle, industrial applications, etc. However, solar panels suffer from low efficiency. Moreover, it can be overcome using the maximum power point tracking (MPPT) technique, and the MPPT is achieved using incremental conductance (Inc‐Cond). The speed of IM can be controlled using IVC with the help of an inverter. The inverter is inherently controlled based on the pulse width modulation (PWM) techniques for reducing the voltage or current harmonics. Here, the proposed CSEQ‐DRC involves two switching sequences such as CSEQ0 and CSEQ1, in every sector region. For every sector region, the appropriate sequence can be operated/selected in the less area of root mean square (RMS) of the stator flux ripples (SFRs) to reduce the line voltage/current profiles of the IM. Consequently, the torque ripples and speed fluctuations are further reduced significantly with the proposed CSEQ‐DRC than the conventional space vector duty ratio control (CSVDRC). In real‐time applications of PV‐fed IVC IM, the interface of the DSPACE DS‐1104 controller is used here to implement for both the proposed CSEQ‐DRC and CSVDRC techniques. Also, the overall performance of the IM drive is improved with the proposed technique as compared to the CSVDRC at different operating conditions.

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An Experimental and Analytical Investigation of the Direct Torque Control Method of a Three-Phase Induction Motor

Alshbib

Abdulkerim

2023

J. Electr. Eng. Technol.

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An Interval Type-2 Fuzzy Based DTC of IMD Using the Hybrid Duty Ratio Control

Cited by 3 publications

References 10 publications

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

Credit Risk Assessment Modeling Method Based on Fuzzy Integral and SVM

An improved performance ofPV‐fedIVCinduction motor drive using clamping sequence duty ratio control

An Experimental and Analytical Investigation of the Direct Torque Control Method of a Three-Phase Induction Motor

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