Title :
Feeder-level fault detection and classification with multiple sensors: A smart grid scenario
Author :
Nan Wang ; Aravinthan, Visvakumar ; Yanwu Ding
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Wichita State Univ., Wichita, KS, USA
fDate :
June 29 2014-July 2 2014
Abstract :
The smart grid initiative requires self-healing distribution systems with more accurate fault detection and classification techniques. A multi-sensor feeder-level fault detection and classification algorithm is presented in this work, based on the techniques of the support vector machine and the principal components. An IEEE 34-bus feeder model with dynamic loading conditions is used to evaluate the developed algorithm. Noise in the three-phase current measurements is applied. The numerical analysis indicates that high accuracies in fault detection and classification are achieved for the proposed algorithm.
Keywords :
IEEE standards; fault diagnosis; power distribution faults; power engineering computing; principal component analysis; sensor fusion; smart power grids; support vector machines; IEEE 34-bus feeder model; fault classification techniques; fault detection techniques; feeder-level fault classification; feeder-level fault detection; multisensor feeder-level fault classification algorithm; multisensor feeder-level fault detection algorithm; principal components; self-healing distribution systems; smart grid scenario; support vector machine; three-phase current measurements; Accuracy; Fault detection; Kernel; Noise; Sensors; Smart grids; Support vector machines; Principal component; classification; distribution feeder fault; smart grid; support vector machine;
Conference_Titel :
Statistical Signal Processing (SSP), 2014 IEEE Workshop on
Conference_Location :
Gold Coast, VIC
DOI :
10.1109/SSP.2014.6884569
