Page 64 - North American Clean Energy March/April 2020 Issue

wind power
Misapplied Derating
How wind farm owners lose hundreds of MWh each year
by Gareth Brown
Turbines have been operated at a fraction of their maximal efficiency for years - but this isn’t necessarily a bad thing. Limiting a turbine’s performance is known as derating, and it can benefit both turbine lifetime, limit noise, and support the successful integration of wind assets into the grid.
While it is accepted that derating turbines comes hand in hand with a reduction in energy generation, derating strategies can often be misapplied, leading to wind farms unnecessarily losing hundreds of MWh each year - leaving tens of thousands of dollars in revenue on the table.
Derating can sometimes occur without the asset owners’ knowledge; as the resulting decrease in performance is usually under 5 percent, derating is easily lost in the
noise of other causes of underperformance (such as windspeed, yaw misalignment,
or mechanical wear-and-tear). Advanced digital tools can identify the patterns of underperformance that indicate the turbine has been unnecessarily derated – and why.
By comparing the times that derating is occurring to other data streams (such as windspeed and air density), it is possible to establish whether derating is necessary, and ensure any subsequent loss in revenue is limited. Artificial intelligence (AI) can drive in-depth data analysis, stopping unnecessary derating, and revenue, from slipping under the radar of wind farm owners.
Turbine data can also be used to analyze why these derates have been misapplied, allowing owners to prevent further lost revenue. Below are three of the most common causes of unnecessary derating that owners must tackle to get the most out of their fleet.
Sensor calibration
Derating can be applied in response to environmental conditions. For example, storm conditions can cause significant damage to active turbines, presenting a major safety risk for the surrounding area. Therefore, derating can be a way to apply the brakes to the rotor in extreme weather.
However, if the turbine “senses” adverse weather conditions when there are none, derating conditions will be switched on unnecessarily. When sensors have not been properly calibrated, control systems might begin derating turbines based on faulty sensor readings that indicate adverse conditions, even in the midst of benign weather.
With any control system, it’s important to make sure that sensors are properly calibrated for the environment. Sensor error can be identified if the turbine data does not match with other data streams for environmental data. While this is an arduous process with traditional data analysis methods, AI can find these errors quickly.
LEADING THE WAY IN WIND ENERGY CONSTRUCTION
www.blackandmcdonald.com
64
MARCH•APRIL2020 /// www.nacleanenergy.com