Power Grid Monitoring through IR Image Processing

Due to increasing electrification (electro-mobility, decarbonisation of heating systems), electricity grids are reaching their capacity limits. Grid extensions take long time-periods, face exuberant bureaucracy and are very expensive.
In order to increase the grid capacity (ampacity), a permanent line monitoring is much faster, easier and cheaper. Since the correlation between the current and the cable temperature is high, this variable is of particular interest.
One method to estimate the cable temperature, is to derive it from optical pictures. IR- and RGB-photos of high-voltage lines are available, both taken at night (IR) as well as taken during the day (IR, RGB) (see example of IR in the appendix).

  • High-voltage-lines are divided into a multiple of three (3, 6, 12) current-carrying phases and one single non-current-carrying earth conductor at the top.

    During the day, cables are cumulating additional temperature due to solar radiation, whereas at night it can be assumed, that the earth conductor has adopted the known outside temperature.

    IR-photos are available in PNG- and CSV-format, RGB-photos in PNG-format.

    Task 1:
    Based on an IR-photo (taken at night) and the known temperature of the earth conductor (outside temperature), the temperatures of the phases are to be determined.

    Task 2:
    Based on an IR- as well as an RGB-photo (taken during the day), the temperatures of the phases are to be determined.

    Task 3: 
    The temperature values (together with date and time) are to be stored and have to be retrievable in a cloud.

    Technologies:

    • Advanced Image Processing
    • Machine Learning, Deep Learning
    • We recommend Python as the programming language
    • Further technologies

    Setup/Inputs (provided by PowerGridNavigators or Supercomputing Systems)

    • Some example photos taken at night (IR) respectively during the day (IR, RGB) together with the outside-temperature
    • Login to the cloud
    • Further aspects

     

  • Further Information

    • 40% theory, 60% implementation
    • Term or master’s thesis, 1-2 students
    • Prior knowledge recommended in
      • Signal and image processing

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