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Webinar – SA Power Network’s High-resolution Cost-effective AI Model for Lightning and Vegetation Risks in Power Grids

6 October @ 1:00 pm - 1:30 pm AEDT
FREE FOR MEMBERS

This presentation will focus on a machine learning framework developed to quantify the probability of asset failure caused by lightning and vegetation. By integrating geospatial datasets and satellite remote sensing sources, the model produces dynamic risk profiles and provides the means for SA Power Networks to move towards more predictive risk mitigation.

Key aspects include:

  • Granular Precision: Assess individual asset risk by fusing crowdsourced and satellite data.
  • Scalable Design: Easily extends to new failure modes and data sources with minimal incremental cost.
  • Actionable Impact: Optimises maintenance and targets capital investment using frequent 16-day monitoring cycles.

Part of the Asset Management in Action Webinar Series – showcasing submissions from the 2026 Excellence Awards.

Click here to register

 

Artur Sokolovsky works across machine learning, data engineering and data strategy, contributing to enterprise ML initiatives and helping to build and maintain Python and Snowflake pipelines for Asset Risk Monetisation, Computer Vision and internal LLM integrations. He has supported the deployment of a dedicated Snowflake Datalab environment and helped establish and facilitate research collaborations with international academic institutions. Artur also works closely with engineering and business teams to encourage internal data adoption and strengthen cross-functional alignment. Beyond his technical work, Artur is actively involved in workplace culture and mentorship initiatives, including contributing to internal peer-recognition programs and assisting with Computer Vision technical challenges for high school STEM outreach.

Details

Venue

  • Online – Register to be sent your link