DocumentCode
13755
Title
Icing load accretion prognosis for power transmission line with modified hidden semi-Markov model
Author
Xin Wu ; Lin Li ; Xiaoming Rui
Author_Institution
Sch. of Energy, Power & Mech. Eng., North China Electr. Power Univ., Beijing, China
Volume
8
Issue
3
fYear
2014
fDate
Mar-14
Firstpage
480
Lastpage
485
Abstract
Ice accretion on power transmission lines is one of the major causes for cable failure in Zhaotong area, Yunnan Province, South China. This study proposes a method to predict the remaining-dangerous time (RDT) of the icing load accretion on an interval of the power transmission lines with modified hidden semi-Markov model (HSMM). Based on the predicted RDT of the cables during ice accretion, the appropriate preventative measures can be scheduled in advance by electric power companies. The estimation model with the learning algorithm of support vector machine for icing load accretion is built through historical icing load accretion data and meteorological conditions first. Then, the estimated icing load accretion sequence can be obtained through the estimation model by forecasting the meteorological conditions. The modified HSMM method can eliminate the possible underflow issue during computation, and be used to build the RDT prognosis model. With the estimated icing load accretion sequence and RDT prognosis model, the authors can predict RDT of the icing load accretion on an interval of the power transmission lines. The developed prognosis algorithm is verified through collected meteorological conditions and icing load accretion data on the Dazheng 73# power transmission line in Zhaotong area, Yunnan Province, South China.
Keywords
freezing; hidden Markov models; power cables; power engineering computing; power transmission lines; support vector machines; RDT 30 prognosis model; South China; Yunnan Province; Zhaotong area; cable failure; electric power companies; estimation model; historical icing load accretion data; icing load accretion prognosis; learning algorithm; meteorological conditions; modified hidden semiMarkov model; power transmission line; remaining-dangerous time; support vector machine;
fLanguage
English
Journal_Title
Generation, Transmission & Distribution, IET
Publisher
iet
ISSN
1751-8687
Type
jour
DOI
10.1049/iet-gtd.2013.0063
Filename
6750592
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