Reducing the cost and risks of offshore wind farms

Manchester scientists are working in collaboration to revolutionise technology to improve the efficiency and lower costs of offshore wind farms.

In 2020 more than 10% of electricity generated in the UK (enough to power 4.5 billion homes) was by offshore wind, and over the last five years, the cost has fallen by 50%, making it one of the cheapest sources of electricity in the UK.

The UK’s offshore wind capacity could rise from 10 GW to 80 GW in the coming decades, creating an industry that has the potential to be worth £2 billion per annum. However, the operation and maintenance of offshore wind assets is challenging, potentially hazardous and require highly specialised skills, which are in short supply.

De-risking offshore wind operations

The Holistic Operation and Maintenance for Energy from Offshore Wind Farms project (HOME-Offshore), funded by UK Research and Innovation, has been set up to explore new technologies to de-risk offshore wind operations, reduce costs and make better use of existing assets.

The project unites expertise across different organisations and subject specialisms to develop solutions using state-of-the-art modelling and data science, machine learning, advanced sensing and robotics.

It's a collaboration between the universities of Manchester, Durham, Warwick, Strathclyde, Heriot-Watt and 16 partner companies and organisations and focuses on three key issues:

Firstly, wind farms are complex systems, involving multiple infrastructures that require expertise from numerous sub-specialisms to work. The University of Manchester, with Durham and Strathclyde universities, used modelling and machine learning to bring together the expertise across multiple disciplines and offer holistic solutions.

Secondly, arranging people to inspect remote sites accounts for as much as 80-90% of the offshore operation and maintenance costs. To address this and the skills shortage – HOME-Offshore examined the value of robots. In addition to investigating robots beneath the waves (to assess subsea structures such as cables), aerial robots (to assess the state of turbines), and robots within the electrical structures (such as offshore substations), the project explored communication, robot design, sensor, and structural problems.
Thirdly, finding and addressing problems early, is key to reducing operation costs. Using advanced sensing coupled with a better understanding of system physics and machine learning, HOME- Offshore explored how to better predict failure.

Improving wind farm maintenance through collaboration

The project’s success resulted in several research outcomes being taken forward as follow-on projects, including the Multi-platform Inspection Maintenance and Repair in Extreme Environments project collaboration between scientists and engineers at the universities of Manchester, Bristol, Royal Holloway London and Royal College of Art’s School of Design and experts at Offshore Renewable Energy Catapult, Thales UK, Plant Integrity and Wootzano Ltd.

The HOME-Offshore project also improved the use of advanced maintenance techniques, such as:

  1. Discovering a new sensing technology that, for the first time, provides a way to measure the physical integrity of critical subsea power cables, accurately forecasting their current and future health.
  2. Using third party providers to manage drone-based turbine maintenance, which could save the industry 1% in costs, worth up to £9 million per annum by 2030, based on value to least investment.
  3. Confirming the feasibility of mobile robots to monitor live HVDC (high-voltage, direct current) substations, enabling real-time inspection data to be conducted in areas with high electromagnetic fields.