View source for Projects:2021s1-13005 Determining Dynamic Line Ratings of Over-Head Transmission Conductors based on Line Tension

== Project Team == 
=== Team Members ===
* Andrew Gross
* Michael Iuliano
* Taimur Abdullah Said Al-Sanaidi

=== Supervisors ===
Principal Supervisor: Wen Soong<br>
Co-Supervisor: David Vowles<br>

=== ElectraNet Sponsors ===
* Josh Smithson
* Ellen Thomas

== Abstract ==
The  ratings  of  power  line  overhead  conductors  are  dependent  on  the actual  current  flow,  ambient  weather  conditions  and  conductor  type. Thermal  ratings  have  historically  been  calculated  using  a  weather-based model, however, other technologies such as tension monitors to measure phase conductor tension can be utilised to derive ratings. ElectraNet  installed  several  tension  monitors  in  the  Riverland  region  to support power flows over the DC interconnector to Victoria. These tension monitors can independently calculate dynamic line ratings and also verify the results produced using the existing weather based rating method. This project continues work commenced in 2020 to develop a method to convert  tension  measurements  into  ratings  (in  Amps  and  MVA)  and  to determine when tension delivers a superior rating outcome to weather.

== Introduction ==
The 2021 iteration of this project continues on from the work completed in the [https://projectswiki.eleceng.adelaide.edu.au/projects/index.php/Projects:2020s1-1540_Determining_Transmission_Overhead_Conductor_Ratings_based_on_Line_Tension project of the same name]<ref name="Proj">[https://projectswiki.eleceng.adelaide.edu.au/projects/index.php/Projects:2020s1-1540_Determining_Transmission_Overhead_Conductor_Ratings_based_on_Line_Tension 2020 Determining Transmission Overhead Conductor Ratings based on Line Tension]</ref> 2020 by Adrian Barone and James Smithson.


== Thermal Factors ==
[[File:Overhead Conductor Heat Balance ENetProj2021.png|frame|left|baseline|upright|The various effects of heating and cooling on an overhead conductor.]]
There are four main factors when considering the various thermal effects on an overhead conductor. These are:<br>
* [https://en.wikipedia.org/wiki/Joule_heating Current Heating](Joule Heating) - Due to resistive and magnetic losses of the conductor material while it is conducting current.  The resistive losses are due to both the conducting material and the increase in the resistance of the conductive material as its temperature increases. 
* [https://en.wikipedia.org/wiki/Solar_energy Solar Heating] - This is heating due to direct radiation from the sun. Typically, direct solar radiation is difficult to calculate as direct and diffuse solar radiation has various challenges in measuring it (expensive for sensors, need regular attention). In some cases where this data isn’t available, global solar radiation is used. 
* [https://en.wikipedia.org/wiki/Convective_heat_transfer Convective Cooling] - Convective Cooling occurs per the effect of the air surrounding the conductor heating, reducing the density of the air around the conductor causing cooler air replaces it. 
* [https://en.wikipedia.org/wiki/Radiative_cooling#:~:text=Radiative%20cooling%20is%20the%20process,and%20continuously%20emits%20electromagnetic%20radiation. Radiative Cooling] - This is the effect of the material emitting thermal radiation, losing heat in the process. A simplified equation is used as the radiation loss is a small fraction of the total cooling.
We do not consider [https://en.wikipedia.org/wiki/Corona_discharge#:~:text=A%20corona%20discharge%20is%20a,of%20plasma%20around%20the%20electrode. Corona Heating] as it is unlikely to occur under typical operation of the conductor, nor [https://en.wikipedia.org/wiki/Evaporation Evaporative Cooling] as while it has a significant effect on cooling, it is challenging to assess along the whole conductor and separated from wind effects, so is ignored.  
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== Useful Links ==
[https://en.wikipedia.org/wiki/Help:Cheatsheet Formatting]<br>
[https://projectswiki.eleceng.adelaide.edu.au/projects/index.php/Projects:2018s1-101_Classification_of_Network_Traffic_Flows_using_Deep_and_Transfer_Learning Winner 1 2018]<br>
[https://projectswiki.eleceng.adelaide.edu.au/projects/index.php/Projects:2018s1-192_Karplus-Strong_Synthesis_of_Sound Winner 2 2018]

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