Predictive Condition Assessment of Power Cables
RISE developed a machine learning tool to prioritize maintenance work on underground power cables by analyzing historical outage data, improving sustainability and reliability of electrical networks.
The health of underground cables can be compromised by factors such as age, faulty connections, and unfavorable soil conditions, among others.
The Solution
We have developed a tool utilizing machine learning to prioritize maintenance work on power cables by analyzing historical outage data. This predictive tool allows network operators to schedule maintenance more effectively, thereby:
- Reducing costs
- Enhancing customer satisfaction by minimizing downtime
Collaboration
The initiative was developed in collaboration with Göteborg Energi and several other Swedish energy companies.
Impact
The project has improved the sustainability and reliability of electrical network operations through smarter, data-driven asset management. By predicting where failures are most likely to occur, utilities can shift from reactive to proactive maintenance strategies.
