Predictive Maintenance of offshore wind farms based on OPC UA and Machine Learning Models

Abstract

Offshore wind farms is playing an essential role in reducing carbon emissions, diversifying energy portfolios, and promoting a green transition toward renewable energy, giving a clean and sustainable source of power. As wind turbines continue to grow in size, this makes the process and maintenance of wind turbines more and more complicated. This shows significant challenges due to harsh environmental conditions such as strong winds. This raises maintenance costs, and the critical need for uninterrupted energy production, which underscores the importance of efficient maintenance strategies for the economic sustainability of offshore wind farms. Predictive maintenance, powered by OPC UA and machine learning, offers an advantage approach to wind turbine maintenance. By analyzing real-time operational , data to anticipate failures before they occur, this contributes to reduce downtime, and optimizes operational efficiency. This paper utilizes a predictive model based on a Random Forest algorithm (RFA). The model is implemented using Spyder environment, the obtained results, validated based on metrics values, are promising.