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New technology developed by scientists uses AI to identify faults in wind turbines, optimizing maintenance and increasing the efficiency of wind power generation.
Researchers have proposed a new technology that can automatically identify, locate and measure damage to wind turbine blades. The solution was presented in a paper published in the journal Scientific Reports, from the group Nature, and has the potential to improve the efficiency of preventive maintenance in wind power generation parks.
The project is the result of the work of scientists who developed a hierarchical machine learning model, called HHMLM (Hybrid Hierarchical Machine Learning Model), which uses acoustic data for structural monitoring. The system is aimed at low-power devices and is compatible with tinyML applications, a branch of artificial intelligence applied to small devices with limited processing capacity.
Intelligent and continuous monitoring of wind turbine blades
The system proposed by scientists works by capturing sounds emitted by internal or external damage to turbine blades. These sounds, called acoustic emissions, are processed by algorithms trained to identify patterns that indicate structural problems.
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During the tests, the new technology was applied to composite wind turbine blades. The researchers simulated various types of damage, such as cracks, impacts and wear caused by the environment, with the aim of training and evaluating the model.
Based on this data, the system was able to identify damage with an accuracy rate of over 96%, outperforming other traditional models that tend to perform poorly. Additionally, the solution is capable of processing information in real time, facilitating a quick response for repairs or replacements.
Scientists seek greater efficiency for wind energy
Wind energy is one of the main renewable sources currently used in Brazil and around the world. Efficient turbine maintenance is essential to ensure the continuous and safe production of this energy. The blades, in particular, are subject to high levels of stress, environmental impacts and natural wear and tear.
According to the authors of the study, the new technology can reduce operating costs, reduce wind turbine downtime and increase equipment lifespan. By automating the inspection process, which currently relies on skilled labor and expensive equipment, the system enables a more efficient and cost-effective approach to the industry.
Future applications and large-scale feasibility
In addition to detecting damage, the HHMLM model is also capable of estimating the type of failure and its exact location using just a single acoustic emission sensor. This represents an improvement over other solutions that require multiple sensors or manual analysis steps.
Scientists point out that the technology can be implemented in wireless sensors, allowing its use in remote or difficult-to-access environments, such as offshore wind farms. This flexibility increases the viability of using the solution on a large scale.
The researchers’ next step is to work on miniaturizing the system and integrating it with cloud monitoring platforms. This will allow the collected data to be accessed remotely and used for faster and more accurate decision-making.
Clean energy supported by artificial intelligence
With the advancement of artificial intelligence and the development of solutions like this, scientists have expanded the possibilities for optimizing renewable energy sources. The application of cutting-edge technology in wind turbines is an example of how innovation can contribute to making the global energy matrix more sustainable.
The study reinforces the role of scientific research in creating tools that aid the transition to a low-carbon economy. The new technology represents an important step in this direction, by offering an intelligent and automated alternative for monitoring and maintaining turbines used in wind power generation.
Source: Scientific Reports
