Case study: Optimising turbine output for PNE

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PNE AG is one of the world’s most experienced renewable energy developers. With active operations in fifteen countries, their understanding of wind (offshore and onshore), photovoltaic and green hydrogen projects is helping to shape the future of renewable energy production. But to achieve their ambitions, PNE require timely data on how their energy production assets are performing.

 

Wind energy: unchecked turbine faults risk big losses

 

Wind energy production can be temperamental. And not just because wind is intermittent and unpredictable. Turbines can develop faults that inhibit performance. These faults often develop silently and are only noticed following arduous manual checks of event logs, or when anomalies are identified in monthly performance reports.

 

Bitbloom’s real-time performance analysis software, Sift, provides a better way. And recently it helped to prevent significant losses for PNE following a turbine fault at a site in Lower Saxony, Germany.

 

The tangible impact of real-time performance monitoring

 

The Boitzenhagen wind farm is a seven-turbine onshore development in Wittingen. Bitbloom’s performance analysis software flagged a high-speed shaft temperature sensor defect that was reducing turbine output. This fault was detected thanks to readily available 10-minute SCADA data combined with machine learning models that quickly detect deviations in performance.

 

The Boitzenhagen turbine fault reduced output to approximately 50% of what could be expected during normal operation. By enabling PNE to quickly identify the fault and take swift remedial action, we helped to avoid losses of approximately €12,500, based on the time it would have taken to discover and repair the fault via the O&M’s monthly performance report.

 

Analysis shows the gear bearing temperature reduction due to sensor defect Screenshot from Sift Explore

This resulted in the reduction of turbine power output (blue dots) Screenshot from Sift Explore

 

“Having Bitbloom’s monitoring in place gives us a robust level of confidence that assets are performing at a high level despite dynamic operating environments – a crucial requirement for building a high-value portfolio.” — Sven Dippel, Executive Vice President – Energy Production, PNE

 

Increase the performance of your renewable energy assets

 

Entrusting performance monitoring to an external supplier is a big decision. Through our flagship products – Sift Explore, Sift Insights and Sift Evolve – we provide machine learning enhanced performance monitoring that’s proven to increase the performance and yield of renewable energy production.

 

Of course, you don’t want to end up in a position where your success as a business is contingent on external suppliers. That’s why we provide an environment where you take ownership of your own data – providing all the training and onboarding you need to build and manage your own analytics in house, instead of putting your faith in outsourced monitoring.

 

With a shared understanding of asset performance, you and your team will be able to understand systems output, dynamically respond to changing requirements with agile analytics and act on errors as they arise – before they cause significant disruption. Your data, your way.

 

Try it for yourself – book a demo

 

We are on a mission to revolutionise renewable energy performance analysis and monitoring. To see what our low-cost analytics platforms could do for you, take a closer look at the capabilities or book a free demo.