TY - GEN
T1 - Short-term prediction of distribution network faults based on support vector machine
AU - Bai, Yuling
AU - Li, Yunhua
AU - Liu, Yongmei
AU - Ma, Zhao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - As the network end of power transmission, the distribution network (DN) directly determines the reliability of electricity energy supply. To predict failure accurately is important for increasing the repair efficiency of DN. Based on the failure data from DN in Beijing, the paper researches short-term DN failures prediction and proposes a fault judgment program based on weather and season factors. Failure is analyzed to determine its most important factors. Through support vector machine (SVM) algorithm and considering the relative meteorological factors, using the classification model predicts the number of failures in DN weekly, and establishes sub region classification forecasting model in week frequency with meteorological influence for DN failures prediction. Through the analysis for the number of DN failures data, we find the main influence factors are temperature, precipitation, wind and other meteorological factors. A short-term prediction program is tested lots of times with the data of DN failure. The practical data in Tongzhou district, Beijing, China, proved the effectiveness, precision and feasibility of the proposed method. The paper software used Matlab2014 and LIBSVM.
AB - As the network end of power transmission, the distribution network (DN) directly determines the reliability of electricity energy supply. To predict failure accurately is important for increasing the repair efficiency of DN. Based on the failure data from DN in Beijing, the paper researches short-term DN failures prediction and proposes a fault judgment program based on weather and season factors. Failure is analyzed to determine its most important factors. Through support vector machine (SVM) algorithm and considering the relative meteorological factors, using the classification model predicts the number of failures in DN weekly, and establishes sub region classification forecasting model in week frequency with meteorological influence for DN failures prediction. Through the analysis for the number of DN failures data, we find the main influence factors are temperature, precipitation, wind and other meteorological factors. A short-term prediction program is tested lots of times with the data of DN failure. The practical data in Tongzhou district, Beijing, China, proved the effectiveness, precision and feasibility of the proposed method. The paper software used Matlab2014 and LIBSVM.
KW - classification prediction
KW - distribution network
KW - fault classification
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85047492210
U2 - 10.1109/ICIEA.2017.8283062
DO - 10.1109/ICIEA.2017.8283062
M3 - 会议稿件
AN - SCOPUS:85047492210
T3 - Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
SP - 1421
EP - 1426
BT - Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017
Y2 - 18 June 2017 through 20 June 2017
ER -