How does combined data add value to our customers’ assets?
Palle List Clausen
Senior Digital Business Architect, Digital Transformation, Business Unit Service
Published on 27th of July 2020
In today’s digital era, data has proven to be increasingly valuable to the control and operational optimisation of assets in the renewable sector. At Vestas, we believe that even more value lies in our ability to combine and connect different types of data from a wide variety of sources. Cloud is one of the keys to enabling this, giving us the ability to perform intelligent service, intelligent control and reporting on our customers’ assets.
But is this anything new? For Vestas, it is the next step on our digital journey. You could say that we invented IoT data acquisition in the industry before it even had a name - after all, we’ve been harvesting data from wind turbines to monitor performance and create better designs for over 20 years.
It’s a never-ending journey though, and we’re always scoping the latest technology to add value to our customers’ business. Only few years ago SCADA was considered to be industrial-only; Today it’s seen as a high value data provider for the entire lifecycle of a plant. This means that it is now feasible to combine traditional commercial data with operational data, effectively connecting the knowledge embedded in these two worlds. By making this SCADA data safely and quickly available, we combine data and know-how in one place – and that’s new.
Because we are now able to combine data from a variety of sources on the fly - in real time, we maximise the potential of our data to add value: If you don’t treat it well, its value is reduced; If you manage data properly and are able to respond and act on it then it maintains its value. We need to leverage our experience and diligence from sourcing steel, glass fiber and other key components in our machine manufacturing.
What data are we talking about? Well, everything really: From performance data to meteorological measurements, from standard IT performance logs for the assets themselves to the supporting infrastructure, including the SCADA system and security.
The next step in adding value to this data is bringing it together and making it available precisely where it’s required, at exactly the right time – for example, to one of our service technicians in the field: We all now wear or carry a digital device which allows the technicians to make data driven decision in the fields.
So, at what point does this data translate into value – and what form does it take? The prime value gain is in the services we are now able to build on to our customers’ assets. There are basically three levels of activity here: We can provide a descriptive visualisation of the data; we can perform predictive analytics to pre-empt and pro-actively deal with potentially costly faults before they happen and conduct prescriptive activities where advanced data analytics give qualified pointers towards more beneficial strategic or operational approaches. This is where machine learning comes into its own, adding yet another layer of value, based on the accumulated data Vestas acquires from its multi-fleet, multi-brand assets around the globe.
But where does this data come from? We are, of course, extremely aware of and sensitive to the fact that we’re using customer assets to retrieve the data we use in the optimisation of their business. We therefore have an obligation to treat this data in a secure manner: Security concerns have always been a major entry barrier into new technologies such as cloud and IoT when dealing with critical infrastructures. But with the development in this new industry, we at Vestas are confident the industry has the ability to pull out the data we require without security risks. There will be a lot of functionality needed on site such as real time control. But there’s a lot of functionality we can pull up into the cloud. It’s a paradigm shift in design and thinking.
It’s an exciting time to be working in this industry and change is always just around the corner. The next paradigm shift we anticipate, is going to push the limits of what is possible in terms of overviewing and optimisation at a fleet wide scale. This data will then be utilized to further minimize operational costs - once again, by combining the office world with the operational side with the performance data coming from the site. And as we begin to apply more Artificial Intelligence and Machine Learning to this data – we will assist in reducing operational costs to improving the value of our customers’ assets over their lifecycles. We’re still really only seeing the tip of the iceberg.