Subsea power cables would be the lifelines of offshore renewable energy. However, safeguarding the reliability of subsea cables is one of the toughest challenges in the industry. All these are possible thanks to developing machine learning, which is changing the future of power cable engineering and maintenance. There is a new level of performance monitoring, fault prediction, and cost efficiency that intelligent systems provide for cable operations under the sea.
Important Subsea Cable Integrity
Offshore power transmission depends on installing subsea cables and making use of the energies produced by wind, tides, and other marine sources. Just a single cable failure could cause millions in losses with extended downtime. The fault in the cable could make up for 77% of the total financial losses offshore wind projects incur when studies are conducted in the industry.
Damage occurs due to poor route planning, environmental stress, and wear and tear. Traditional methods of inspection are very manual, slow, and expensive. That is where machine learning steps in.
Machine Learning: The Game Changer
Machine learning teaches the systems how to learn from data and make predictions on actions. In their application to the subsea cables, it will be able to provide real-time insights and predictions of faults before they happen to help an operator take preventive actions.
How It Works
Data from cable sensors—temperature, pressure, vibration, etc.—is fed into machine learning models. These models then detect anomalies that suggest potential damage or inefficiencies. The system can:
- Predict cable fatigue and thermal overloads
- Optimise cable route planning by analysing historical failure data.
- Suggest ideal burial depths to reduce external mechanical impact.
- Automate fault detection and maintenance scheduling.
Case Study: Predictive Maintenance in Action
Leading offshore wind farm operations in the North Sea applied machine learning in predictive maintenance. From five years of actual operational data, the system predicted a 91% success rate of a fault as far as 28 days ahead. Hence, it was possible to preemptively change cable sections instead of waiting for failure, achieving a 32% cost reduction in maintenance and 60% less unplanned outage time.
This use case is a great power that can be put into data-driven power cable engineering and maintenance methods.
Beyond Maintenance Gains
Machine learning is not only repairing, but it is also evolving project planning and achieving at the Offshore Transmission Conference level:
- Improved design decision-making with historical project data
- Real-time cable performance optimisation
- Reduced downtime and response times during faults
- Less OPEX due to predictive asset monitoring
With more and more companies moving towards digital twin and AI-based planning, machine learning tends to become the key to the integrity of subsea infrastructure.
5th Annual Subsea Cable Installation, Asset Management & Reliability Forum
The 5th Annual Subsea Cable Installation, Asset Management & Reliability Forum, organised by the Leadvent Group, is a venue for those innovations to grow and bear fruit. Set in the year 2025, the gathering will bring together the leaders in power cable engineering and maintenance to exchange perceptions, innovations, and solutions.
Significance of the Forum
Opportunities of a different kind can also arise via our Leadvent Startup Innovate initiative. This allows startups and innovators in subsea technology to showcase their products, pitch concepts, and interact with decision-makers from the offshore wind power landscape. The marketing partner/exhibitor routes give high visibility and strategic access to a target audience. From sensor technologies to smart monitoring tools, this event constitutes a platform for innovations that help make subsea operations smarter, safer, and more efficient.
The leading Offshore Transmission Conference will study deeper into issues such as:
- Risk assessment on cable during its whole lifecycle
- Burial of cables and route optimisation
- Cost reduction and life extensions
- Innovation in repairs, operation, and maintenance
- AI and machine learning in monitoring systems
Why Attend!
With over 35 speakers and 100+ attendees from leading organisations, the forum will provide the tools to apply in practice:
- Case study presentations with lessons to take away
- Strategic panel discussions with great minds
- Roundtables touching on the future of cable asset management
- Networking receptions with key stakeholders of this industry
Spotlight on Innovation
Companies will showcase their latest technologies. These innovations mirror the growing importance of machine learning in power transmission and cable reliability.
This year's forum will also emphasise stakeholders' approaches to lifecycle issues—from the design of the cable to fault detection—with data-led accuracy.
Conclusion
The use of machine learning in subsea cable systems is redefining power cable engineering and maintenance. By focusing on prediction, the approach reduces downtime, increasing reliability and minimising cost concerns. The offshore transmission community must remain informed about current developments.
The Offshore Transmission Conference at the 5th Annual Subsea Cable Forum will present an exclusive opportunity to witness these revolutionary changes. Get involved, learn about the newest offerings, and help lay the framework for the future of cable reliability.
Frequently Asked Questions
Q: How does machine learning mitigate subsea cable failure?
A: It detects early signs of wear, pressure shifts, or temperature spikes, thus allowing proactive repairs prior to complete cable failure.
Q: Is machine learning cost-effective for small-scale offshore schemes?
A: Yes. Scalable solutions are available now, and AI and ML have become a viable options even for medium-sized installations.
Q: Does it mean machine learning will replace human skills in the cable maintenance field?
A: Not replacing, but augmenting. The specialists will focus on decision-making, while machine learning will assist with data crunching and prediction.
