Classification and regression analysis using support vector machine for   classifying and locating faults in a distribution system

Sophi Shilpa GURURAJAPATHY; Hazlie MOKHLIS; Hazlee Azil Bin ILLIAS

Makale detayları

Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system

Yazarlar:

Sophi Shilpa GURURAJAPATHY | Hazlie MOKHLIS | Hazlee Azil Bin ILLIAS

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 6, pp.3044-3056

Belge Tipi:

Makale

Yayın Tarihi:

11/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000734538

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Various fault location methods have been developed in the   past to identify the faulty phase, fault type, faulty section, and distance.   However, this identification is commonly conducted in a separate manner. An   effective fault location should be able to identify all of these at the same   time. Therefore, in this work, a method using a support vector machine (SVM)   to identify the fault type, faulty section, and distance considering the   faulty phase is proposed. The proposed method uses voltage sag magnitude of   the distribution system as the main feature for the SVM to identify faults.   The fault type is classified using a directed acyclic graph SVM. The   possible faulty sections are identified by estimating the fault resistance   using support vector regression and matching the voltage sag data in the   database with the actual voltage sag data. The most possible faulty sections   are ranked using ranking analysis. The fault distance for the possible   faulty sections is then identified using support vector regression analysis   and its overfitting or underfitting issues are addressed by the proper   selection of a regularization parameter. The feasibility of the proposed   method was tested on an actual Malaysian distribution system. The results of   faulty phase, fault type, faulty section, and fault distance are analyzed.   The performance of the proposed method is compared with various other   intelligent methods such as the artificial neural network, deep neural   network, extreme learning machine, and kriging method. The test results   indicate that the faulty phase and fault type yield 100 % accurate   results. All the faulty sections are identified and the proposed method   obtains reliable fault location.

Makale detayları

Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system

Yazarlar: Sophi Shilpa GURURAJAPATHY | Hazlie MOKHLIS | Hazlee Azil Bin ILLIAS

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 6, pp.3044-3056

Belge Tipi: Makale

Yayın Tarihi: 11/2018

Belge Dili: Türkçe

Document ID (Org No.): 2000734538

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Sophi Shilpa GURURAJAPATHY | Hazlie MOKHLIS | Hazlee Azil Bin ILLIAS

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 6, pp.3044-3056

Belge Tipi:

Makale

Yayın Tarihi:

11/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000734538

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632