Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems

Thukaram, D; Khincha, H.P.; Vijaynarasimha, H.P.

doi:10.1109/tpwrd.2005.844307

Cited by 354 publications

(188 citation statements)

References 19 publications

(13 reference statements)

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“…The system is trained and tested with "k" different training and test clusters whereas for each case the "k" performance measures could be obtained. Thus, the arithmetic mean of the obtained "k" performance measures is calculated to determine the success of the cross validation [27]. This study has been performed using the functions of MATLAB Statistics and Machine Learning Toolbox and MATLAB Neural Network Toolbox [28,29].…”

Section: Results and Conclusionmentioning

confidence: 99%

Skin Lesion Classification using Machine Learning Algorithms

Özkan

Köklü

2017

ijisae

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Melanoma is a deadly skin cancer that breaks out in the skin's pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH 2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.

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Section: Results and Conclusionmentioning

confidence: 99%

Skin Lesion Classification using Machine Learning Algorithms

Özkan

Köklü

2017

ijisae

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“…Applications within power systems using SVM have been reported in (Moulin et al, 2004;Thukaram et al, 2005;Janik et al, 2006). In (Bishop, 2008), a classifier based on radial basis function (RBF) network and SVM has been proposed and compared for classification of four classes of PQ disturbances.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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“…Intelligent techniques are among the most popular approaches. In [4][5][6][7], an artificial neural network (ANN) was used for fault localization in the distribution network. In [4,5], the fundamental frequency components of voltage and * Correspondence: mosba86@yahoo.com.my current were employed.…”

Section: Introductionmentioning

confidence: 99%

“…In [4][5][6][7], an artificial neural network (ANN) was used for fault localization in the distribution network. In [4,5], the fundamental frequency components of voltage and * Correspondence: mosba86@yahoo.com.my current were employed. A combination of ANN and support vector machine (SVM) was proposed in [5], whereas in [6,7], the high frequency components were extracted from the faulted waveform to facilitate fault location.…”

Section: Introductionmentioning

confidence: 99%

Studies on the application of wavelet families for a high impedance fault location algorithm in a distribution network

Ali¹,

Bakar²,

Tan³

et al. 2016

Turk J Elec Eng & Comp Sci

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Abstract:Detecting high impedance faults in a distribution network is a great challenge and locating one is even more challenging. This paper will present an enhanced high impedance fault location algorithm based on the database technique. This paper is an extension of a faulty section identification algorithm developed previously. However, in this paper, the performance of the previous algorithm is investigated first in terms of different sampling rate and window size. Then these parameters will be used as a standard parameter to study the effectiveness of different types of mother wavelets. Each mother wavelet will be used to extract important features of a voltage signal from a single measurement point in a typical 38-node underground distribution network.

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Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems

Cited by 354 publications

References 19 publications

Skin Lesion Classification using Machine Learning Algorithms

Skin Lesion Classification using Machine Learning Algorithms

Power quality event classification: an overview and key issues

Studies on the application of wavelet families for a high impedance fault location algorithm in a distribution network

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