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The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title: Icing thickness prediction model for overhead transmission lines
Author: Ming, Yue
View Online: njit-etd2018-052
(xii, 62 pages ~ 1.2 MB pdf)
Department: Department of Electrical and Computer Engineering
Degree: Master of Science
Program: Electrical Engineering
Document Type: Thesis
Advisory Committee: Zhou, MengChu (Committee chair)
Liu, Qing Gary (Committee member)
He, Lianghua (Committee member)
Date: 2018-12
Keywords: Transmission line failures
Ice thickness prediction
Availability: Unrestricted
Abstract:

Failures in a large electric power system are often inevitable. Severe weather conditions are one of the main causes of transmission line failures. Using the fault data of transmission lines of Shaanxi Power Grid from 2006 to 2016, in conjunction with meteorological information, this paper analyses the relationship between the temporal-spatial distribution characteristics of meteorological disasters and the fault of transmission lines in Shaanxi Province, China.

In order to analyze the influence of micro-meteorology on ice coating, a grey correlation analysis method is proposed. This thesis calculates the grey relational between ice thickness and micro-meteorological parameters such as ambient temperature, relative humidity, wind speed and precipitation. The results show that the correlation between ambient temperature, wind speed and ice thickness is bigger than others. Based on the results of grey correlation analysis, a Multivariate Grey Model (MGM) and a Back Propagation (BP) neural network prediction model are built based on ice thickness, ambient temperature and wind speed. The prediction results of these two models are verified by the case of ice-coating of Shaanxi power grid. The results show that the prediction errors of the two models are small and satisfy the engineering requirement. Then a realistic case is carried out by using these two models. An icing risk map is drawn according to the results.


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