| Title |
Development of Classification Model of Power System Fault by Using PMU Big-Data |
| Authors |
강성범(Sung-Bum Kang) ; 고백경(Baek-Kyeong Ko) ; 남수철(Su-Chul NAM) ; 최영도(Yong-Do Choi) ; 김용학(Yong-Hak Kim) ; 전동훈(Dong-Hoon Jeon) |
| DOI |
https://doi.org/10.5370/KIEE.2019.68.9.1079 |
| Keywords |
WAMS; PMU; Big-Data; CNN; Fault Classification |
| Abstract |
Recently, innovative techniques in artificial intelligence such as machine learning have emerged to efficiently process huge amounts of big data delivered from PMUs to WAMS. Through processing raw data and analyzing big data, It delivers highly useful and valuable system status information to system operators. The types of machine learning vary depending on the usage, but the CNN (Convolution Neural Network) model is mainly used for the post analysis and fault detection(classification) in the power system. In this paper, based on PMU big data, we study the power system fault classification model by using CNN Model. Using Convolution neural network model based on KERAS, the database for each fault type was built and supervised learning was conducted for the model. The constructed model was verified with test data and the validity of the model was verified by inputting the actual power system fault data for the trained model. As a result, developed model classified correctly for the actual fault. |