ThermOHL

Temperature estimation of overhead line conductors is an important topic for TSOs for technical, economic, and safety-related reasons (DLR/ampacity, sag management ...). It depends on several factors, mainly transit, weather and the conductor properties. ThermOHL is a python package to compute temperature and/or ampacity in overhead line conductors.

Features

The temperature of a conductor is estimated by solving a heat equation which describes how temperature evolves over time, taking into account different power terms that either heat or cold the conductor (see next picture from CIGRE[1]).

Two heat equations (a more complete, third one is under development) are available:

• one with a single temperature for the cable;
• another with three temperatures (core, average and surface temperature) for more precise computations.

Each of these equations can be used with a set of pre-coded power terms from the literature :

• one using CIGRE recommendations [1];
• one using the IEEE standard [2];
• two others from RTE departments.

Solvers derivated from heat equations can compute steady-state temperature or ampacity, and transient temperature. The set of parameter required depends on the power terms used, and default values are provided.

References

• [1] Stephen et al., Thermal behaviour of overhead conductors. CIGRE, Study committee 22, working group 12, 2002. https://e-cigre.org/publications/detail/207-thermal-behaviour-of-overhead-conductors.html.
• [2] IEEE, Standard for Calculating the Current-Temperature Relationship of Bare Overhead Conductors. IEEE Std 738–2012 (Revision of IEEE Std 738–2006, Incorporates IEEE Std 738–2012 Cor 1–2013), 2013. https://doi.org/10.1109/IEEESTD.2013.6692858.

Users

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Environment

ThermOHL is using pip for project and dependencies management. You need a compatible version of python (3.8 or higher). You may have to install it manually (e.g. with pyenv). Then you may create a virtualenv and activate it.

Set up thermohl

To install the package using uv, execute the following command:

    uv pip install "thermohl @ git+https://github.com/phlowers/thermohl"

Use it ! You can report to the user guide section.

    import thermohl
    print(thermohl.__version__)

Developers

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Install the development dependencies and program scripts via

  uv pip install -e .
  uv sync --group dev

Build a new wheel via

  uv build --wheel

This build a wheel in newly-created dist/ directory

Building the documentation with mkdocs

First, make sure you have mkdocs and the Readthedocs theme installed.

If you use uv, open a terminal and enter the following commands:

  uv sync --group docs

Then, in the same terminal, build the doc with:

• mkdocs serve - Start the live-reloading docs server.
• mkdocs build - Build the documentation site.
• mkdocs -h - Print help message and exit.

The documentation can then be accessed locally from http://127.0.0.1:8000.

Simple usage

Solvers in thermOHL take a dictionary as an argument, where all keys are strings and all values are either integers, floats or 1D numpy.ndarray of integers or floats. It is important to note that all arrays should have the same size. Missing or None values in the input dictionary are replaced with a default value, available using solver.default_values(), which are read from thermohl/default_values.yaml.

Example 1

This example uses the single-temperature heat equation (1t) with IEEE power terms and default values to compute the surface temperature (°C) of a conductor in steady-state regime along with the corresponding power terms (W.m^-1).

from thermohl import solver

slvr = solver.ieee(dic=None, heateq='1t')
temp = slvr.steady_temperature() 

Results from the solver are returned in a pandas.DataFrame:

>>> print(temp)
           t   P_joule  P_solar  P_convection  P_radiation  P_precipitation
0  27.236417  0.273056  9.64051      6.587129     3.326436              0.0

Example 2

This example uses the same heat equation and power terms, but to compute the line ampacity (A), ie the maximum power intensity that can be used in a conductor without exceeding a specified maximal temperature (°C), along with the corresponding power terms (W.m^-1). We can see that, for three different ambient temperature, we have three distinct ampacities (and the lower the ambient temperature, the higher the ampacity).

import numpy as np
from thermohl import solver

slvr = solver.ieee(dict(Ta=np.array([0., 15., 30.])), heateq='1t')
Tmax = 80.
imax = slvr.steady_intensity(Tmax)

>>> print(imax)
             I    P_joule  P_solar  P_convection  P_radiation  P_precipitation
0  1606.398362  83.737734  9.64051     66.750785    26.627459              0.0
1  1408.025761  64.333311  9.64051     50.884473    23.089348              0.0
2  1184.741847  45.547250  9.64051     36.234737    18.953023              0.0