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

N, Venkataramana Naik; Singh, S. P.; Panda, Anup Kumar

doi:10.1109/tpel.2020.2965722

Cited by 20 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A hybrid duty ratio control (HDRC) technique using interval type-2 fuzzy-based DTC (IT2FDTC) has been suggested [50]. The rules of the type-2 fuzzy interval is designed to have a quick settling period and little IMD speed/capacitor voltage overshoots.…”

Section: Type-2 Fuzzy Logic System Block Diagrammentioning

confidence: 99%

“…This presents a so-called "new look" at type-2 fuzzy sets (T2 FSs) and systems and asserts, quite boldly, that the new view is better than the old one. A thorough examination of the connections between this unique representation of a T2 FS and (at the absolute least) the well-known T1, blur, and weight representations is not present in [50], nor is there any discussion of expanding the new representation from one to multiple MF parameters.. Numerous academic works have shown the usefulness of EKF, and several new, improved versions have also been suggested [51].…”

Section: Type-2 Fuzzy Logic System Block Diagrammentioning

confidence: 99%

See 1 more Smart Citation

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: Type-2 Fuzzy Logic System Block Diagrammentioning

confidence: 99%

Section: Type-2 Fuzzy Logic System Block Diagrammentioning

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

“…Considering the influence of complex factors, the sustainable development evaluation of renewable energy incubators is complicated and uncertain. Interval type-II fuzzy number plays a momentous role in coping with indefinite factors and deriving robust results [32]. This technique has been widely used in many fields, such as air quality evaluation, supplier assessment, etc.…”

Section: Interval Type-ii Fuzzy Numbersmentioning

confidence: 99%

Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

2021

View full text Add to dashboard Cite

With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.

show abstract

“…The fuzzy logic, or more generally the treatment of uncertainties, is one of the classes of artificial intelligence [29], it is introduced to ameliorate the performances and effectiveness of the proposed 24 sectors DTC technique of the DFIM fed by a threelevel NPC inverter. However, the fuzzy logic technique has the capability to control nonlinear uncertain systems even in the case where no mathematical model is available for the control system [30].…”

Section: Hysteresis Controller Of Torque and Flux Based On Fuzzy Logicmentioning

confidence: 99%

24 sectors DTC control with fuzzy hysteresis comparators for DFIM fed by 3 level inverter

Jibril

2021

Preprint

View full text Add to dashboard Cite

This article presents 24 sectors direct torque control (DTC) with fuzzy hysteresis comparators for the doubly-fed induction motor (DFIM) using a three-level neutral point clamped (NPC) inverter. The designed DTC technique of the DFIM combines the advantages of the DTC strategy and fuzzy logic controller. The reaching conditions, stability, and robustness of the DFIM with the designed DTC technique are guaranteed. The designed DTC technique is insensitive to uncertainties, including parameter variations and external disturbances in the whole control process. Finally, the designed DTC technique with fuzzy hysteresis comparators is used to regulate the electromagnetic torque and the flux of the DFIM fed by the three-level NPC inverter and confirms the validity of the designed DTC technique. Results of simulations containing tests of robustness and tracking tests are presented.

show abstract

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

Cited by 20 publications

References 25 publications

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

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

Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

24 sectors DTC control with fuzzy hysteresis comparators for DFIM fed by 3 level inverter

Contact Info

Product

Resources

About