As solar activity reaches its peak in 2025, the risk of geomagnetic storms that could knock out parts of our electricity grid increases. Combined with extreme weather, energy infrastructure faces new challenges. However, by digitalising transformer stations and investing in predictive maintenance, we can make electricity grids more resilient than ever before.
As the energy transition accelerates, resource-efficient maintenance becomes increasingly important. Modern electricity networks with increasing proportions of renewable energy and distributed production place new demands on flexibility and reliability. By implementing digital infrastructure for predictive maintenance, conditions are created to handle future challenges.
With the help of more connected assets in Ellevio's substations, Gomero is contributing to one of Sweden's largest energy companies being able to work with more digitised maintenance inspections and workflows.
Predictive maintenance represents an important change in the energy sector. By replacing reactive and schedule-based strategies with data-driven, proactive monitoring, energy companies can increase operational reliability, reduce costs, and extend the lifespan of critical infrastructure.
Electricity grid operators worldwide are striving to connect critical assets remotely to streamline and digitize their maintenance. To support this development, Gomero is deepening its collaboration with IBM. Combining Gomero's advanced data collection and analysis technology with IBM's asset management platform creates an innovative and data-driven approach to substation maintenance.
Sulfur hexafluoride (SF6) has long played a crucial role in power grids. The gas's exceptional insulating properties make it indispensable for transformer stations and other high-voltage systems. However, SF6 is also one of the most potent greenhouse gases, making its management a key environmental issue.
Through digitalised maintenance, inspections and data reporting, personnel safety can be significantly improved, especially with predictive systems that anticipate potential faults using data collection and advanced analysis.
