TY - GEN
T1 - Fault Classification and Location Identification in a Smart Distribution Network Using ANN
AU - Usman, Muhammad Usama
AU - Ospina, Juan
AU - Faruque, Md Omar
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/21
Y1 - 2018/12/21
N2 - This paper presents a novel approach to classify and locate different types of faults in a smart distribution network (DN). The proposed method is able to classify all types of faults that can occur in a DN and then based on fault type, it can identify the approximate fault location (FL) with a high accuracy. The method is based on artificial neural networks pattern recognition which uses data from μPMUs/smart meters placed at different locations in a DN. The proposed technique needs fault-on voltages of all the nodes connected to the end of line/branches in order to classify and locate different types of faults. The method is tested on a modified IEEE-37 bus system with distributed generation along with dynamic loading conditions and varying fault resistances. Both balanced and unbalanced fault types are applied to the system. An accurate classification of 100% is achieved when classifying all fault types and above 99% accuracy is achieved when identifying the approximate fault location.
AB - This paper presents a novel approach to classify and locate different types of faults in a smart distribution network (DN). The proposed method is able to classify all types of faults that can occur in a DN and then based on fault type, it can identify the approximate fault location (FL) with a high accuracy. The method is based on artificial neural networks pattern recognition which uses data from μPMUs/smart meters placed at different locations in a DN. The proposed technique needs fault-on voltages of all the nodes connected to the end of line/branches in order to classify and locate different types of faults. The method is tested on a modified IEEE-37 bus system with distributed generation along with dynamic loading conditions and varying fault resistances. Both balanced and unbalanced fault types are applied to the system. An accurate classification of 100% is achieved when classifying all fault types and above 99% accuracy is achieved when identifying the approximate fault location.
KW - Artificial Neural Network (ANN)
KW - Fault Classification
KW - Fault Location (FL) Identification
KW - Supervised Machine Learning
KW - μPMUs
UR - https://www.scopus.com/pages/publications/85060816822
U2 - 10.1109/PESGM.2018.8586471
DO - 10.1109/PESGM.2018.8586471
M3 - Conference contribution
AN - SCOPUS:85060816822
T3 - IEEE Power and Energy Society General Meeting
BT - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PB - IEEE Computer Society
T2 - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Y2 - 5 August 2018 through 10 August 2018
ER -