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Proceedings ArticleDOI

A new protection algorithm for synchronous generators using artificial neural networks

TLDR
The proposed scheme is able to discriminate internal and external faults of synchronous generator, consists of feature extraction using eigenvalues and fault classification using artificial neural networks.
Abstract
This paper presents a new protection scheme for synchronous generator using an artificial neural network. The proposed scheme is able to discriminate internal and external faults of synchronous generator, consists of feature extraction using eigenvalues and fault classification using artificial neural networks. The performance of algorithm was tested using artificial neural networks and simulation results show the proposed scheme is able to identify and classify all types of faults of synchronous generator. The proposed approach has the ability to detect the generator winding faults which are close to neutral.

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Citations
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Journal ArticleDOI

Synchronous generator stator protection using a fuzzy logic‐based voltage‐controlled overcurrent protection scheme

TL;DR: In this article, a new voltage-controlled overcurrent protection scheme is proposed as a backup protection method for the differential protection method, which is designed to operate in conditions in which differential protection fails to do so and is implemented by fuzzy controllers.
Dissertation

Fault diagnosis for transmission lines using chromatic processing

TL;DR: In this article, an alternative method of fault diagnosis and location using chromatic methodology to extract information from current and voltage waveforms from a simulated transmission line with different fault conditions is proposed.
Journal ArticleDOI

Incipient Fault Diagnosis in Stator Winding of Synchronous Generator: A CMFFLC Technique

TL;DR: A combined strategy of moth–flame optimization algorithm and fuzzy logic controller (FLC) for incipient fault diagnosis of the synchronous generator is proposed to analyse the beginning issues exhibited by an asynchronous generator under different situations like healthy and unhealthy conditions.
References
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Journal ArticleDOI

An introduction to computing with neural nets

TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI

An introduction to computing with neural nets

TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI

An artificial neural network based digital differential protection scheme for synchronous generator stator winding protection

TL;DR: In this article, a new artificial neural network (ANN) based digital differential protection scheme for generator stator winding protection is described. But the scheme only uses two ANNs, one for fault detection and the other for internal fault classification.
Journal ArticleDOI

Transformer Differential Protection Using Principal Component Analysis

TL;DR: In this paper, a new algorithm for transformer differential protection, based on pattern recognition of the differential current obtained as the phasor sum of the current-transformer secondary currents, was proposed.
Journal ArticleDOI

ANN-based novel fault detector for generator windings protection

TL;DR: In this article, an artificial neural network (ANN) based internal fault detector algorithm for generator protection is proposed, which uniquely responds to the winding earth and phase faults with remarkably high sensitivity.
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