Offshore Wind Journal makes reference to our project for working towards the reduction in the cost of energy from offshore wind

Optimising the operation of wind farms can bring about a significant reduction in the cost of energy from offshore wind. This is precisely the main idea of ​​the article published in Offshore Wind Journal, in which the journalist David Foxwell makes reference to our Project for working towards the fulfillment of that objective.

ROMEO project seeks to reduce O&M costs by developing and demonstrating an O&M information management system and an analytics platform, capable of improving decision-making processes while allowing a transition from corrective and calendar-based maintenance to condition-based maintenance strategies.

A flexible and interoperable cloud-based and internet of things platform will provide an advanced analytics ecosystem for failure diagnosis and prognosis models to better understand the real-time behaviour of the main components of turbines under operational conditions, maximising their life span and minimising O&M costs. The project will also develop third-generation condition monitoring systems for some components and low-cost structural condition monitoring systems.

Within the framework of the project, IBM Research is developing predictive machine learning technologies for a wide range of projects. IBM’s Dr Dorothea Wiesmann, Head of Cognitive Computing & Industry Solutions Department  at IBM’s Zurich lab, explained in an interview that the company is developing advanced machine learning models for predictive maintenance of wind turbine components.

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