We teamed up with the IBM® Cloud™ Garage consultancy in Nice to deploy our P4A advanced asset monitoring platform for wind turbines in the IBM Cloud.
Emerging with an initial version in just 8 weeks, we proved that our platform is both robust and flexible.
Thanks to their highly scalable architecture, secure cloud resources and IBM Watson tools the cooperation with IBM readies Performance for Assets for further growth.
“Effective predictive maintenance could significantly help to extend the life and value of assets. But while lots of energy companies have monitoring solutions that generate huge amounts of data, most of it goes unused”
To reduce costs and increase production levels for asset owners, P4A saw an opportunity to harness Internet of Things (IoT) and analytics solutions to enable predictive maintenance and to boost asset performance.
In an eight-week engagement, P4A worked with the IBM Cloud Garage to build a minimum viable product (MVP) that monitors wind turbines and provides real-time alerts to technicians via a virtual assistant.
By 2020, the European Union (EU) is aiming to boost the proportion of energy generated from renewable sources to at least 20 percent.
In 2016, wind overtook coal as the second largest form of power generation capacity in the EU, and it’s fast catching up to the leader: gas.
As demand for wind energy grows, producers have the chance to make huge gains—if they can tackle the issues that limit production levels.
Apart from weather conditions, which wind energy producers cannot control, the other main factors that affect output are asset performance and availability.
Until now, most wind turbine owners have had little to no insight into the condition of their machines. Even though their end-of-design lifetime is usually close to 20 years, original equipment manufacturers (OEMs) for wind turbine components will typically guarantee operation throughout a 12 to 15-year long-term service agreement (LTSA). Once the LTSA comes to an end, wind energy companies struggle to find insurance for components. At the same time, owners lack visibility of how well assets are performing, meaning they could be operating at well below maximum throughput for years at a time.
Performance for Assets (P4A) saw an opportunity to extract much greater value from wind turbine assets. Laurent Rakoto, Data Analyst at the company, explains: “Effective predictive maintenance could significantly help to extend the life and value of assets. But while lots of energy companies have monitoring solutions that generate huge amounts of data, most of it goes unused. Some include alerts or warning systems, but don’t give field technicians enough information about what actions they should take next. This means they miss the opportunity to significantly raise their output by making sure that every component works to its best possible potential.”
In response, P4A created Wintell, an advanced monitoring system that combines sophisticated analytics with field experts’ process knowledge in preventive, corrective and predictive maintenance, condition monitoring, testing inspection and certification and data mining to provide actionable intelligence. P4A chose to focus on wind farms as an initial use case.
In developing the concept, the P4A team realized that it needed support from technology experts and a flexible, scalable cloud platform to bring Wintell to market successfully. Rakoto adds: “To launch Wintell commercially we needed to extend it so that it could better accommodate big data and machine learning workloads. We also wanted to add new functionality, so we looked for partners that could help us create a marketable solution with leading-edge capabilities.”
Read the full interview on IBM’s website.