Park Chul Young, Shin Chang Sun, Park Myung Hye, Lee Seung Bae, Park Jang Woo

KIPS Transactions on Computer and Communication Systems, Vol. 6, No. 11, pp. 445-452, Nov. 2017
10.3745/KTCCS.2017.6.11.445,   PDF Download:
Keywords: Accelerometer, Electric Pole, Generalized Additive Model, Machine Learning, Meteorological Conditions
Abstract

Electric pole is a supporting beam used for power transmission/distribution which accelerometer are used for measuring a external force. The meteorological condition has various effects on the external forces of electric pole. One of them is the elasticity change of the aerial wire. It is very important to perform modelling. The acceleration sensor is converted into a pitch and a roll angle. The meteorological condition has a high correlation between variables, and selecting significant explanatory variables for modeling may result in the problem of over-fitting. We constructed high deviance explained model considering multicollinearity using the Generalized Additive Model which is one of the machine learning methods. As a result of the Variation Inflation Factor Test, we selected and fitted the significant variable as temperature, precipitation, wind speed, wind direction, air pressure, dewpoint, hours of daylight and cloud cover. It was noted that the Hours of daylight, cloud cover and air pressure has high explained value in explonatory variable. The average coefficient of determination (R-Squared) of the Generalized Additive Model was 0.69. The constructed model can help to predict the influence on the external forces of electric pole, and contribute to the purpose of securing safety on utility pole.

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