Fuzzy inference system-based solution to locate the cross-country faults in parallel transmission line

Kumar, A. Naresh; Sanjay, C.; Chakravarthy, M.

doi:10.1177/0020720919830905

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…It is often used to solve uncertain inference problems 23 – 27 . Naresh Kumar et al proposed a fault location method based on fuzzy inference for the uncertainty of faults, compared with the existing location methods, the proposed solution has been obtained higher fault location accuracy 28 . Song et al applied fuzzy inference to fault diagnosis, and verified the effectiveness of introducing fuzzy inference through the study of uncertainty interference in mechanical structure faults 29 .…”

Section: Introductionmentioning

confidence: 99%

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

Tang

Huang

Zhu

et al. 2022

Sci Rep

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Aiming at the problem of poor transient performance of the control system caused by the control uncertainty of the undertrained neural network, a neural network compensation control method based on fuzzy inference is proposed in this paper. The method includes three control substructures: fuzzy inference block, neural network control block and basic control block. The fuzzy inference block adaptively adjusts the neural network compensation control quantity according to the control error and the error rate of change, and adds a dynamic adjustment factor to ensure the control quality at the initial stage of network learning or at the moment of signal transition. The neural network control block is composed of an identifier and a controller with the same network structure. After the identifier learns the dynamic inverse model of the controlled object online, its training parameters are dynamically copied to the controller for real-time compensation control. The basic control block uses a traditional PID controller to provide online learning samples for the neural network control block. The simulation and experimental results of the position control of the magnetic levitation ball show that the proposed method significantly reduces the overshoot and settling time of the control system without sacrificing the steady-state accuracy of neural network compensation control, and has good transient and steady-state performance and strong robustness simultaneously.

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Section: Introductionmentioning

confidence: 99%

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

Tang

Huang

Zhu

et al. 2022

Sci Rep

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“…The FES [29][30][31][32] has been used in Lab VIEW. The FES designing is used for protection of transmission lines [33][34][35], but these do not protected the DC3PT faults. Fault classification and location using FES in three-phase circuit lines are explained in [36].…”

Section: Introductionmentioning

confidence: 99%

A single-end directional relaying scheme for double-circuit transmission line using fuzzy expert system

Kumar

Sanjay

Chakravarthy³

2020

Complex Intell. Syst.

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The faults occurring in different sections are difficult to identify using the traditional techniques. This paper investigates a fuzzy expert system for directional relaying, classification, and location of faults in double-circuit transmission lines. The current magnitudes measured at only one terminal of the double-circuit transmission line are used to compute discreet Fourier coefficients. Thus, this scheme does not involve any communication channel. The presented fuzzy expert system is achieved from the structure of MAMDANI system in LabVIEW software. Test case studies show the effectiveness of the presented scheme. The simulation results attest that the directional relaying, classification, and location estimation is very accurate. This scheme is adaptive to the change of fault location, fault resistance, fault inception angle, and fault type.

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“…The location of faults by considering fundamental component of currents and voltages in transmission line has been widely studied in Ref [6]. Further, different fault location algorithms have been presented in DCTPTL based on fuzzy inference system (FIS) [7][8], support vector machines [9], artificial neural networks (ANN) [10] and k-nearest neighbor algorithm [11]. Presence of a variety of individual computing methods [7][8][9][10][11] available for locating the DCTPTL is still having certain problems which limit their applicability.…”

Section: Introductionmentioning

confidence: 99%

“…Further, different fault location algorithms have been presented in DCTPTL based on fuzzy inference system (FIS) [7][8], support vector machines [9], artificial neural networks (ANN) [10] and k-nearest neighbor algorithm [11]. Presence of a variety of individual computing methods [7][8][9][10][11] available for locating the DCTPTL is still having certain problems which limit their applicability. Hybridization of technologies can solve the Int J Artif Intell ISSN: 2252-8938  limitations of individual computing applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system based evolving fault locator for double circuit transmission lines

Kumar¹,

Sridhar²,

Kumar³

et al. 2020

IJ-AI

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<p>Evolving faults are starting in one phase of circuit and spreading to other phases after some time. There has not been a suitable method for locating evolving faults in double circuit transmission line until now. In this paper, a novel method for locating different types of evolving faults occurring in double circuit transmission line is proposed by considering adaptive neuro-fuzzy inference system. The fundamental current and voltage magnitudes are specified as inputs to the proposed method. The simulation results using MATLAB verify the effectiveness and correctness of the protection method. Simulation results show the robustness of the method against different fault locations, resistances, time intervals, and all evolving fault types. Moreover, the proposed method yields satisfactory performance against percentage errors and fault location line parameters. The proposed method is easy to implement and cost-effective for new and existing double circuit transmission line installation</p>

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Fuzzy inference system-based solution to locate the cross-country faults in parallel transmission line

Cited by 16 publications

References 20 publications

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

A single-end directional relaying scheme for double-circuit transmission line using fuzzy expert system

Adaptive neuro-fuzzy inference system based evolving fault locator for double circuit transmission lines

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