Research and Application of Fire Forecasting Model for Electric Transmission Lines Incorporating Meteorological Data and Human Activities

Jiazheng Lu, Jun Guo, Li Yang, Tao Feng and Jie Zhang

Mathematical Problems in Engineering, 2016, vol. 2016, 1-10

Abstract:

Recently, there is a rise in frequency of fires which pose a serious threat to the safety operation of electric transmission lines. Several ultrahigh voltage (UHV) electric transmission lines, including Fufeng line, Jinsu line, Longzheng line, and Changnan line, showed many times tripping or bipolar latching caused by fire disasters. Fire disasters have tended to be the biggest threat to the safety operation of electric transmission lines and even can cause power grid collapse in some severe situations. Researchers have made much research on fires forecasting. However, these studies are mainly concentrated on predicting fires based on measured or forecasting meteorological data and do not take into account the effect of human activities. In fact, fire disasters have a very close relationship with human activities. In our research, a fire prediction model is proposed incorporating meteorological data as well as human activities. And this model is applied in Hunan province and Anhui province, which seriously suffer from fire disasters. The results show that the model has good prediction precision and can be a powerful tool for practical application.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2016/9828676.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2016/9828676.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9828676

DOI: 10.1155/2016/9828676

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().