SMART DEVICE RAM - 1

RAM-1®

The smart IoT device RAM-1 is intended for remote monitoring and advanced analysis of power grids. It offers machine learning as well as communication via 4G, 5G or LoRaWAN. If attached to a gapless surge arrester with a continuous operating voltage above 1 kV the RAM-1 device can measure the resistive component of the leakage current. This innovative method of extracting the resistive component of the leakage current conforms to the standard IEC 60099-5. Power grids become Smart with the use of the RAM-1 device, which improves the operating process and the reliability and stability of the electricity transmission and distribution networks.

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SMART DEVICE RAM - 1, installation on insulative bracket, wildlife protection

Advantages & Technical data

  • Remote monitoring and advances analysis of electricity transmission and distribution networks,
  • Measurement of resistive component of leakage current of surge arrester,
  • Surge counter, ambient temperature and temperature of device,
  • Collapse or tilt of pole/tower, micro location, navigation to location,
  • Instant indication of critical information, autonomous operating,
  • Machine learning based on collected information,
  • Installation on existing and new gapless surge arresters,
  • Simple installation, low price, without communication costs.
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Delivery & Installation & Special features

To ensure optimal working conditions it is essential to first check the presents of 4G and 5G networks. If LoRaWAN networks are used a questionnaire regarding the adequacy of the networks must be filled out.

The installation guide for installing on surge arrester can be found on the back side of the packaging.

The end customer can pick between different versions of RAM-1 which all are capable of machine learning. To get a broader array of anomalies in the power networks we welcome all our end customer to cooperate with us in creating a machine learning database. Only with the help of our end customers can we list and identify as many anomalies as possible. By identifying the anomalies we can make corrections to optimise and improve the working of the power grid thus lowering the carbon foot print of the produced electricity as well as stabilising the network.

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