Clir’s new peer-to-peer trending tool makes it easy to detect anomalous behavior and trending over time. For example, it lets users spot anemometer drift and other sensors requiring recalibration. It also detects subtle differences in power or pitch curve behavior.
Software as a service company, Clir Renewables, has added a new feature to the company’s Data Exploration Environment. Clir Explore, is a unique tool in the renewables space, comprised of 12 different features to identify asset under-performance.
The latest addition, P2P, enables peer-to-peer trending analysis for wind turbines.
Clir Explore lets users view and manipulate streamlined data sets, which narrow in on specific performance issues. The tool also makes complex analysis simple by providing the specific variables to use when identifying different kinds of anomalous turbine behavior.
Comparing concurrent turbine data is a vital part of a complete wind-farm analysis.
“Before our clients started using Clir, they didn’t have the data model or tools to get their hands around numerous wind farms. Not only do you need to know what kind of underperformance you are looking for, but you also need to know which variables to use to complete the analysis,” said Gareth Brown, CEO of Clir Renewables.
The P2P tool allows the user to compare any two turbines in their wind farm and select the variable they want to compare. Among the variables available are power, wind direction, wind speed, blade pitch angle, and generator speed. The results can be based on time period, nacelle position, or wind directions, which means the user can easily identify if patterns are time depended or direction dependent. The scatter plot and overlaying linear regression, makes it easy to spot outliers.
“With our workbooks, users can use Clir’s catalogue of visualizations — and create their own visualizations using the Clir data model,” Brown added.
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