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Artificial Intelligence and the Winds of Change

The windmill was invented by the Persians sometime around the ninth century, and ever since, humankind has been harnessing the power of the wind to do everything from grinding grain and pumping water to, more recently, generating electricity. Now, with the growing focus on climate change mitigation and the need to broaden the portfolio of energy-generation assets, wind power has risen to the forefront of investment and installation. Growth in the wind power industry has taken two distinct forms in recent years: more turbines being installed in more locations, and ever-larger turbines in the pursuit of greater economies of production. 

 

The wind industry is growing rapidly

In 2022, growth of global wind power was 265 TWh (14% increase) according to the International Energy Agency (IEA), and now totals more than 2100 TWh. The Global Wind Energy Council counts 341,000 wind turbines in operation worldwide, of which approximately 74,000 are in the U.S. This generation capacity allowed the U.S. to forgo about 348 million metric tons of CO2 emissions in 2023 alone, with the US Department of Energy estimating a cumulative 12.3 gigatons that could be saved by 2050.

As impressive as these statistics sound, there is, though, another important component to the story: the challenge of operating and maintaining the turbines that provide this increasingly significant portion of the world’s electric power. Worth noting in particular is that not only is the number of wind turbines in operation growing significantly each year, but the size of these expensive assets is growing as well, both physically and in terms of generating capacity. That is because larger turbines generate power at lower unit cost per KwH, all of which increases the importance of effectively and safely maintaining each turbine tower, while also increasing the challenge of doing so.

To the extent that progressively increasing the size of wind turbines (by as much as 60% over the past decade) makes maintenance more challenging—both onshore and particularly offshore—alternative approaches to maintenance and repair are also becoming increasingly critical. The global wind health and safety organization G+ reports that there were 1,679 incidents worldwide in 2023, nearly double the rate of the previous year. Of this total, 560 occurred at operating sites, while the remaining two thirds happened on wind construction projects. A key takeaway from these accident statistics and growth trends (in both number and size of turbines) is that better ways  of maintaining wind power assets are required, methods that reduce the need for climbing and visually inspecting turbines, blades, etc. Artificial intelligence (AI) is uniquely equipped to provide that solution, enabling safer and more economical operations. 

 

AI improves wind turbine operation and maintenance efficiency

In recent years, the wind power industry has adopted a number of digitalization and AI strategies that have enabled safer, more automated approaches to maintenance challenges, modeling complex performance data relationships, predicting potential failure outcomes, and delivering improved asset performance management (APM). AI solutions can proactively monitor and mitigate issues with wind turbines, whether blade defects, damaged pitch bearings, structural fatigue/corrosion, or other issues. This helps reduce downtime and maintenance costs and allows operators to service turbines based on real-time conditions rather than fixed timelines. AI can reduce maintenance costs by up to 15% per year, while also enhancing reliability, and increasing revenue up to 5% by improving maintenance schedules and increasing generation forecasting accuracy.

Predictive AI identifies patterns in operational performance data, providing operators and managers with advance notice of deviations from normality that might otherwise go unnoticed. This allows problems to be addressed before performance degrades further or failures occur. Prescriptive AI goes one step further, identifying the likely causes of performance problems and suggesting actions that can be taken to further assess the issue, as well as maintenance steps to address the problem.

 

Case studies in AI-enabled turbine maintenance

A few examples of AI’s growing role in wind turbine maintenance demonstrate the value:

Pitch bearing failure: Pitch bearings on wind turbines are subjected to demanding operating conditions and can be very costly to replace. Using data from existing sensors on the turbine, pitch bearing health can be continually assessed and failures predicted ahead of time. Failures can be predicted with over 90% accuracy up to six months in advance, saving upwards of $150,000 through optimized repair scheduling.

Yaw angle misalignment: Yaw misalignment (the difference between turbine angle and the wind’s direction) can significantly reduce turbine generation efficiency and output, increase the likelihood of failure, and increase operation and maintenance (O&M) expenses. A machine learning-based solution that’s capable of detecting yaw misalignment—-using only two months of historical data—-can detect yaw misalignment of five degrees or more with 96% accuracy.

Power production monitoring: Wind turbine power production depends on many disparate factors, including asset age, maintenance condition, and operating practices, all of which make it challenging to maximize performance while keeping costs down. Quickly identifying underperforming turbines while also providing the most likely root cause of failures and suggesting corrective actions can help to ensure economical long-term performance.

 

Wind power and AI: Natural partners in sustainability

Wind power will be a significant element of our future global energy landscape. AI technology is an essential component for ensuring end-to-end reliability while striking an optimum balance between risk, cost, and performance. To achieve this goal, AI must be integrated into the processes, models, and workflows used to manage turbine performance. Analyzing real-time performance data that combines engineering, operations, maintenance, and reliability outcomes with explanatory context is critical for identifying the information necessary to optimize performance of these expensive assets throughout their full lifecycle.

Learn more about Avathon’s AI-enabled solutions and how they can enhance the performance of your renewable generation assets

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