The Overhead Line Inspection Data Challenge

Challenge context

TenneT is a leading European grid operator. We are committed to providing a secure and reliable supply of electricity 24 hours a day, 365 days a year, while helping to drive the energy transition in our pursuit of a brighter energy future – more sustainable, reliable and affordable than ever before.

At TenneT we are at the heart of the Dutch and German energy transition. We are working hard to expand our transmission capacity and support the electrification of industry, transport and households, enable the growth of renewable energy production and connect our customers. To make it happen faster we need radical innovation.

Periodic inspections are key to assess the health of our assets and monitor the surrounding environment to identify potential hazards. Contemporary inspections however, are resource intensive and limited in scope. We believe that the digital transformation of our inspection and monitoring efforts holds immense value potential that can not only improve our resource allocation, but also boost our grid capacity, prolong our asset life and enhance safety and reliability.

Challenge details

We are looking for software solutions that generate valuable insights and enable data-driven decisions from data gathered through smart inspections of our overhead transmission lines.

The primary objectives of this challenge are:

1. Enhance Congestion Management: Develop innovative solutions that analyze data to predict and manage congestion on transmission lines, ensuring a stable and reliable power supply.
2. Optimize Maintenance: Propose technologies that utilize inspection data to forecast maintenance needs, prioritize interventions, and extend the lifespan of transmission infrastructure.
3. Efficient Resource Utilization: Create systems that utilize insights from inspection data to optimize the deployment of resources, reducing operational costs and improving overall efficiency.

Key focus areas are:

1. Data Analytics and Machine Learning:
   • Predictive analytics for identifying potential congestion points and maintenance needs.
   • Machine learning algorithms that can learn from historical data and improve decision-making over time.
2. Visualization and Reporting Tools:
   • User-friendly dashboards and visualization tools that provide actionable insights to operators.
   • Reporting systems that can automatically generate maintenance schedules and resource allocation plans.

Integrated solutions that comprehend both data acquisition and analysis may be considered, but we have a strong preference to be able to split the two and focus our challenge on the data analysis part. The data input your solution should be able to incorporate may be based on various acquisition methods ranging from advanced sensors both remote and direct, automated aerial and ground-level inspections and increasingly advanced inspection tools to support our personnel.

We envisage modular, flexible and expandable building blocks that are integrated in our own data platform and facilitate increased data availability, unlock data synergies and support automation.

What to expect?

Our plan is to launch a Proof of Concept for promising solutions within two months to demonstrate their value and prepare for full-scale implementation if successful.