Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System

Tawafan, Adnan Hasan; Sulaiman, Marizan; Ibrahim, Zulkifilie

doi:10.11591/ij-ai.v1i2.425

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…There are different clustering techniques such as k-means clustering, fuzzy c-means clustering, mountain clustering and subtractive clustering. If there is no clear idea how many clusters there should be for a given set of data, subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters [19].…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

A Secure ANFIS based Relay for Turbo-Generators Phase Backup Protection

Aziz¹,

Elsamahy²,

Moustafa³

et al. 2016

IJEECS

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This paper presents a new advanced methodology as a solution for the problem due to the current setting of Relay (21) when it is set to provide thermal backup protection for the generator during two common system disturbances, namely a system fault and a sudden application of a large system load. Keywords: adaptive neuro fuzzy inference system, distance protection, phase backup protection, turbogenerator, dynamic performance, simulationCopyright © 2016 Institute of Advanced Engineering and Science. All rights reserved. IntroductionProtective relaying provides important defense for power grid operation. Conventional backup protection depends on local electrical information to make relevant decisions, and cannot securely distinguish an internal fault from heavy load during flow transfer [1]. This may cause cascading trip events and accelerate power system failure. In addition, the complex setting principle of conventional backup protection may induce hidden failures caused by setting mistakes [2], which would increase the risk of system instability during a disturbance [3,4].Moreover, some of the researchers have proved the existence of a potential problem due to the current setting of Relay (21) when it is set according to the present standards and recent related publications for generator thermal backup protection against transmission line uncleared faults, it is found that the current setting of the Relay (21) for generator thermal backup protection restricts the over excitation thermal capability of the generator [5]. Such a restriction does not allow the generator to supply its maximum reactive power during such events. Thus, the necessity of revising the Relay (21) reach to ensure a secure performance for the relay during major system disturbances was initiated, which would allow the generator to fulfill the system requirements during such events to ensure adequate level of voltage stability.The Relay (21) element is typically set at the smallest of the following three criteria [6]: 1. 120% of the longest line with in-feeds. 2. 50% to 67% of the generator load impedance (Z load ) at the rated power factor angle (RPFA) of the generator. This provides a 150% to 200% margin over generator full load.3. 80% to 90% of the generator load impedance at the maximum torque angle (MTA) of the relay setting (typically 85°) (Z GCC ).In particular situations when the generator distance phase backup protection relay is mainly required to provide thermal backup protection for the generator against transmission line faults, which are not cleared by transmission line relays, Relay (21) is set directly according to the second criterion (which is the setting considered during the investigations of this paper). The

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Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

A Secure ANFIS based Relay for Turbo-Generators Phase Backup Protection

Aziz¹,

Elsamahy²,

Moustafa³

et al. 2016

IJEECS

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“…The Adaptive Network-based Fuzzy Inference System (ANFIS) introduced by Jang [13,14] is a well-known neural fuzzy controller with fuzzy inference capability had been implemented in various works [8][9][10][11][12]. ANFIS is based on fuzzy logic modeling and use ANN as the learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

Shahbudin

Kassim

Mohamad

et al. 2019

IJEECS

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This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classiﬁcation tasks using extracted features based on S-transforms methods. The ANFIS model with a ﬁve-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal. Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that the right type and number of Membership Functions (MFs) are used in the classification task.

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“…This fault has a small current ranging from a few mA to tens of amperes [4]. The failure of HIFs detection leads to serious threat in electric shock to human beings and potential fire hazards [5].…”

Section: Introductionmentioning

confidence: 99%

An improved method for high impedance fault detection in medium voltage networks

Ravlić¹,

Marušić

Havelka

2017

Teh. vjesn.

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Original scientific paper This paper presents a set of rules for an intelligent algorithm of high impedance fault detection by observing the phase to ground voltage, phase to phase current and harmonics of fault current. Several techniques to detect high impedance faults already exist for example current injection in the neutral, superposing voltage signals, harmonic analysis, neural networks. The main disadvantage of all methods is that the normal system activity may cause false tripping of the protection device. Our goal is to give a set of rules for an algorithm that can make a secure decision that a particular feeder event is high impedance fault. The set of rules was carried out from simulation results simulated in MATLAB. The proposed approach was trained and tested with 200 data sets and provides excellent results. Keywords: fault detection; Fourier transform; high impedance fault; medium voltage network Unaprijeđena metoda detekcije visokoomskih kvarova u mrežama srednjeg naponaIzvorni znanstveni članak Ovim člankom predstavljen je skup pravila za inteligentni algoritam detekcije visokoomskih kvarova promatranjem faznih napona i struja, kao i harmonika struje kvara. Nekoliko metoda detekcije visokoomskih kvarova već postoji kao što su npr. injektiranje struje, superpozicija signala napona, harmonička analiza, neuronske mreže. Glavni nedostatak svih postojećih metoda detekcije visokoomskih kvarova leži u tome da zaštitni uređaji prepoznaju normalne sklopne operacije u mreži kao visokoosmski kvar (neželjena prorada zaštitnih uređaja). Cilj istraživanja bio je postaviti skup pravila za inteligentni algoritam koji može donijeti sigurnu odluku da li je do kvara došlo ili ne. Navedeni skup pravila je dobiven na temelju rezultata simulacijskog modela razvijenog u programskom paketu MATLAB. Navedeni pristup je ispitan s 200 skupova ulaznih podataka te daje odlične rezultate.

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Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System

Cited by 15 publications

References 13 publications

A Secure ANFIS based Relay for Turbo-Generators Phase Backup Protection

A Secure ANFIS based Relay for Turbo-Generators Phase Backup Protection

Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

An improved method for high impedance fault detection in medium voltage networks

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