Page 14 - North American Clean Energy January/February 2019 Issue

wind power
Using AI to Pinpoint the New “Normal”
in Wind Turbine Performance
by Jessica Hamm
WE EXPECT MACHINES TO BE MORE CONSISTENT THAN
we humans, but the truth is, machine behavior can vary daily. Fortunately, or unfortunately, these variations are more likely to be due to external factors like temperature and wear, rather than which side of the bed it woke up on.
Maintenance managers have long depended upon condition-based monitoring (CBM) to alert them to this type of degradation, and the needed repairs. However, we now have a more reliable way to predict failures; by understanding deviations from normal behavior, Arti cial Intelligence (AI) powered predictive maintenance has been able to detect some failures months in advance, with up to 90 percent accuracy. Imagine the di erence these sorts of insights could make when scaled across operations.
The business case
Wind turbines are complicated machines that populate vast expanses of water, farmland, and mountain ranges. According to the American Wind Energy Association (AWEA), a new turbine is installed every two and a half hours, presenting both a tremendous means to generate clean, renewable energy, and a new liability in terms of maintenance and repair. Deployed turbines are at the mercy of the elements, including precipitation, lightning strikes, wind gusts, and ice, in addition to wear from normal operation. is means that every turbine operates under unique conditions and stresses, limiting the accuracy of traditional condition monitoring.
With over 58,000 turbines, and a total of 90 GW of capacity now operating across America, it’s easy to see how maintenance costs are compounded by scale. A single, out-of-service 1.5MW turbine can cost an operator over $2000 USD per day, in lost production. If needed, an unexpected crane rental pushes costs upwards of $350K per week. O shore repairs are even more complicated, and can cost over $1M per week to rent a jack boat.
Fortunately, advances in AI are helping to address these problems by providing more accurate analytics to identify suboptimal performance, and quantify the risks of failure. Take, for instance, the crane rental example: With a better understanding of the failure timelines for multiple turbines in a wind farm, the operator can pinpoint the precise window of opportunity to hire a crane for several repairs at once — spreading the expense across multiple assets. With wind capacity expected to nearly triple by 2030, strategies are needed today, to drive e ciency across the industry.
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Why is AI needed for predictive analytics?
Predictive maintenance involves deploying sensors to monitor critical components of a physical asset. Data is then analyzed for known and unknown clues that indicate imminent failures. Traditional approaches require technicians and subject matter experts to monitor asset health in real time, often reacting to issues rather than anticipating them. Due to the distributed nature of wind farms, limited worker expertise, and rapid industry growth, this existing methodology is unsustainable.
ese aren’t the only issues; predictive maintenance requires historical data and failures to create a model which can be used to predict asset health and operating states of a given asset. In traditional approaches, these models rely on pre-programmed rules, and physics-based models, all painstakingly calculated by human analysts. Not only is it a struggle to scale these models across large operations, they take a great deal of time to design and implement, and perform poorly in predicting edge cases that occur under extreme or unusual operating parameters. Most unfortunately, if a single variable of the asset is changed,