A key component of “Big Data” is, naturally enough, data – plenty of data. But where does all this data actually come from and what exactly happens to it?
At innogy there are many examples of how data can be put to beneficial use – even if that benefit is primarily one of recognition or new insights. But such insights will help make power generation and distribution more efficient, secure, sustainable and, ultimately, more affordable too.
Data stream from the monitoring systems of our wind turbines
One such data stream flows, for instance from the monitoring systems of our wind turbines, known as “Condition Monitoring Systems” (CMS). These systems record such details as pressures, temperatures and fluctuations in the driveline of the turbines. The readings of hundreds of sensors are registered several times a second, creating a data volume that no person could possibly evaluate “by hand”.
To ensure this flood of data can not only be mastered but also linked to that of other data streams (such as weather data), special analysis algorithms are applied. Such algorithms are capable of performing statistical analysis and also taking operational experience into account.
Developing the algorithms and interpreting the results requires a high degree of expertise and programming skill. This kind of data analysis is no “all-inclusive, trouble-free package”. It has to be constantly questioned and reviewed. At the moment, algorithms and methods for wind power analysis are being developed by various project teams and incorporated into our systems.
Such “Big Data” analysis is used, amongst other things, to optimise fleet management. As a result, any damage to plant or equipment can be detected at an early stage to kickstart optimal maintenance measures. This in turn reduces costs and outage times. Any overloading of turbines can also be detected and rectified in a targeted manner. This preserves the service life of plant and equipment and can even lengthen the operating life of some turbines.
Artificial intelligence meets customer communications
An other successfully realized big data project at innogy is the improvement of customer care using artificial intlligence. An AI system presorts the costumer communications in the retail business so the rihgt request reaches the right costumer care agent. This was a benchmark for the whole innogy group.
NILM identifies consumption of any machine
Another example is NILM which stands for “Non-intrusive Load Monitoring” – or monitoring of voltages and consumption levels without having to penetrate any closed systems. This can be useful for commercial and industrial customers who want to retain their data within their existing systems.
Even then, NILM helps identify power guzzlers and unusual voltage patterns in a timely manner, which enables customers to improve their energy management.
City, country, river – why innogy is interested in waterways
In the case of the EU project “AMBER”, it is about artificial barriers in river courses. This includes transverse structures such as barrages and weirs, with the help of which hydropower is generated. The data delivered by Amber helps with our planning of power plants for renewable generation in an ecologically sustainable manner.
Handling data correctly
With so much data available, the way we deal with it is particularly important, especially where personal data is concerned. At innogy, Uwe Bargmann, our data protection officer keeps a beady eye on this. “Big Data projects have changed the way we do things but not totally redefined the playing field. “I think we take data protection and data security very seriously at innogy”, says Bargmann. Especially when you look at the business models coming out of our US-based company. innogy has opportunity here to establish a point of difference for, as our data protection expert knows only too well: “Customer trust is a key asset and can become a competitive advantage for us, if we go about things the right way.”