Power grid operators such as Bjäre Kraft, Telge Nät, and Kraftringen faced recurring challenges with unplanned automatic shutdowns (trips) in the power grid. These disruptions led to costly outages, inefficient maintenance, and a stressful work environment. To anticipate and prevent these interruptions, a more advanced, data-driven solution was needed. Traditional monitoring and fault identification methods often fall short in preventing these issues in time.

We helped dLab develop and implement a powerful machine learning-based software for power grid analysis. By combining our technical expertise with dLab’s domain-specific knowledge, we created a system that:

• Monitors and provides early warnings to predict outages and disturbances in the power grid.
• Forecasts interruptions and disturbances, enabling scheduled preventive maintenance and minimizing the risk of downtime.
• Combines domain-specific knowledge with data-driven insights to maximize the accuracy of predictions.

The implementation of dLab’s software resulted in:

• Reduced unplanned shutdowns thanks to proactive troubleshooting.
• More efficient maintenance schedules, contributing to a safer and more productive work environment.
• Cost savings and improved operational stability for power grid operators.