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Over the last few years, offshore wind has flourished, driven by its popularity in several parts of the world. One member-based trade association representing the entire wind energy sector named 2019 the best year in the industry’s history. It acknowledged that the COVID-19 pandemic less impacted it than many other areas of the energy market.
Additionally, smart technology allows squeezing the most electricity out of offshore plants by ensuring that they operate effectively and preventing unscheduled downtime.
Intelligent Wind Power
Offshore power plants are regulated by operations centers usually located onshore. If a fault arises, the center will either patch the defect remotely or assign a turbine repair technician. However, on-site repairs face a range of problems. As turbines are typically situated miles from the ground, servicing includes a boat ride to the site, with inclement weather and sea conditions frequently delaying repairs. It comes at a high rate, too. Artificial Intelligence (AI) and Machine Learning (ML) are now being saved, helping to avoid unscheduled outages by tracking and predictive maintenance. Every second, wind turbines relay thousands of signals. Some of these signals are sent to the power plant controller to assist with load balancing and make constant power changes.
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Predictive maintenance precision and all AI and ML-related techniques are highly dependent on vast quantities of data from a large number of turbines to detect any suspicious trends. The framework draws on both historical and real-time evidence to enhance the precision of its forecasts. By acting preventively based on these observations, offshore wind turbine operators can reduce unscheduled downtime and maximize energy generation to reach the maximum possible performance.
A 360-Degree View
The integration of AI, ML, and digital twin technology, which produces a digital replica of the facility, allows operators a 360-degree view of the wind turbine to determine risks and handle potential maintenance needs. For example, these technologies can help predict the turbine’s life expectancy or the emergence of material fatigue of the component. In this way, appliances may be replaced proactively as part of routine maintenance, reducing the risk of facing up to the time, expense, and operating effect of reactive repairs.
They often encourage operators to perform tests to assess how the increase in electricity production will impact the turbine components’ durability. In addition to financial and efficiency gains, the use of remote technology also adds to health and safety, as technicians do not need to be sent for emergency maintenance under extreme weather conditions.
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