Qi Li

Date of Award


Level of Access Assigned by Author

Campus-Only Thesis

Degree Name

Master of Science (MS)


Electrical and Computer Engineering


Mohamad Musavi

Second Committee Member

Richard Eason

Third Committee Member

Paul Villeneuve


Electricity usage and generation has been growing significantly over the past few years. Consequently, the ageing power grid has to carry more power while construction of new power lines becomes increasingly costly. For the integration of intermittent renewable energy, developing a reliable and accurate measurement tool is important to maximize power line utilization. In this research, distributed PDs have been utilized to collect transmission line thermal and other related information. Along with environmental data such as wind speed and weather ambient temperature. This information has been used for training and testing of a neural network predictor and IEEE 738 standard predictor, to make accurate conductor temperature and ampacity prediction. The generated I-T curve can allow operators to make informed decision on transmission line utilization.