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The wind of digitalisation is blowing

Introduction

A fresh breeze has been blowing through the industrial world for some time. After electrification and automation, digitalisation is the next stage of development, which will have sustainable impact and not only on our energy systems. One of the main components of digitalisation is the collection of data. There were 33 ZB (Zettabyte = 1021 Bytes) of data generated in 2018 alone and that value is projected to increase to 175 ZB by 2025. The pile of DVDs necessary to store that data would reach the moon 23 times (1).

As an integral part of our modern energy systems, wind turbines and parks generate huge amounts of data through integrated sensors. Starting with output via maintenance needs all the way to outside variables like grid parameters and weather conditions, the installed sensors can accurately reflect the current situation of a wind park. One of the key challenges for the wind energy sector is to interpret such data in intelligent ways, and thus employ it to unlock potential for wind power, in particular, and energy systems overall.

Digitalisation of the wind energy sector: aims and potential

Digitalisation is a key steppingstone for the complete integration of wind energy into energy systems (figure 1). System integration here means firstly the improvement and extension of grid services offered by turbines (e.g., frequency and voltage stabilisation), as well as levelling of fluctuating power production using energy storage, load management, and the intelligent teaming up of wind energy with other production sources. The ultimate goal is the coupling of the electricity sector with other sectors of the energy industry. All these elements of system integration require great amounts of data exchange and interactivity between the various components of an energy system.

Digitalisation harbours great economic potential (figure 1). For example, data collected by various sensors to permanently monitor the condition of a turbine in combination with condition specific maintenance can lead not only to greater output through lesser shutdowns, but also tends to reduce maintenance cost. While improved meteorological forecasting models and intelligent turbine control add to improve electricity output, a databased design has the potential to lower construction cost significantly through the avoidance of over-engineering (manufacturing at unnecessarily high-quality levels). Further, such improved design combined with condition specific maintenance can also elongate lifetime expectancy for turbines. There is then the possibility of added value to the kilowatt hour because databased analysis of the electricity market aids in securing higher compensation.

Figure 1: Wind energy and digitalisation (based on (2)).

Optimisation of operations through digitalisation

These days almost every turbine is continuously remotely monitored through a so-called SCADA-System (Supervisory Control and Data Acquisition). The collected information includes data regarding the condition of the turbine, but also all relevant operational parameters such as current output, rotation and wind speed. Usually, every turbine manufacturer uses their own, specific online portal for data monitoring. A unified, modern software solution is therefore needed, particularly from the viewpoint of operational management. It would enable communication between interfaces of almost all turbine manufacturers and types and thusly allow for centralised data collection, analysis, monitoring and management.

A modern, expandable operational management software such as the one in use at 4initia GmbH is at the same time a key element in unlocking the potential for operational optimisation created by increasing levels of digitalisation in wind energy.

Specifically, to name but a few:

  • Prompt identification of faults through individually configurable alarm signals
  • Automated notification of the maintenance company in case of a faul
  • Continuous monitoring and analysis of the performance curve
  • Automated monitoring and verification of shutdowns due to environmental standards from planning permissions (e.g., bat or shadow casting shutdowns)
  • Integration of meteorological forecast models for better maintenance planning
  • Optimised turbine inspection using mobile devices and mobile inspection software
  • Autonomous inspection of rotor blades employing drones
  • Unified non-availability alerts for direct marketing, originating straight from the software

For optimum use of the data, it must not only be available in digital format, but also be easily linked to other information. Using the example of the optimised turbine inspection above it becomes clear that even small changes can generate a distinct added value. Employing a connected, mobile inspection software, faults can be immediately stored and traced in a database. In comparison, the provision of information from a traditional assessment in PDF format takes a lot more effort.

Essential for increased exhaustion of the above potential is the maximum utilisation of interfaces to exchange information, such as the direct marketing interface that 4initia GmbH helped the BWE (Bundesverband Windenergie e.V.) develop (3). However, more work is needed in the area of interfaces to link as many stakeholders in wind energy as possible. Although modern turbines are highly connected there is a lot of underdeveloped potential with service and maintenance companies, forecasting service providers and grid operators. Among the latter, transmission system operators have already developed a guideline to receive extensive data from the wind farm operators: the system operation guideline (SO GL) (4). The association of wind energy is currently proposing a bidirectional interface that would enable farm operators to benefit from valuable information as well, rather than just delivering it. 4initia is currently actively contributing to a task force set up by the BWE to address this issue.

Another vital role in the optimisation of turbine operation is played by intelligent tools to efficiently analyse and utilise the giant mass of data generated (Big Data). Although some progress has been made, further science and development is needed to even begin tapping into the world of possibilities accompanying digitalisation. To name one example, the project ALICE, which is funded by the Federal Ministry of Education and Research, is investigating how wind turbines could learn their own operating data through machine learning. The objective being that entire wind farms could optimally adapt to changing conditions in the environment or load and, thus, deliver maximum output via perfect controlling processes (5).

Condition Monitoring

The concept of condition monitoring implies continuous observation of the nick of a machine (in this case a wind turbine) to improve safety and machine efficiency through imminent identification of faults and breakages. The basis for this is the measuring and documentation of an array of physical quantities (e.g., temperature, forces, rotation speed and acceleration) using countless sensors placed all over the body of the turbine. Partly, the data supplied by those sensors is analysed in real-time, so discontinuities in the running of the turbine are flagged up and answered to immediately. This also allows for automated responses such as emergency shutdown in the case of critical turbine conditions. While such continuous recording of parameters demands extremely high levels of data management, it also serves as complete documentation of long-term development and, thus, as a maintenance and condition log of the respective turbine.

Although not able to predict and prevent spontaneous part shutdowns or breakages (i.e., the force breakage of a shaft), the estimation of part life expectancy based on analysis of material wear and tear is a key element of condition monitoring. Thus, parts can be exchanged just before they may cause a fault, but also not before the end of their life span. Condition-oriented maintenance, as based on condition monitoring, could soon make preventive or reactive maintenance a thing of the past as it has been shown to increase economic feasibility of wind parks through reduction of turbine standstill to a minimum.

Digital Lifecycle log

As wind turbines in their structure and operation are getting ever more complex, data collection in the face of digitalisation is increasing exponentially. Therefore, digital lifecycle logs offer the possibility to structure, link, and efficiently manage all interdisciplinary information regarding a turbine. These logs can be pictured as “information twins”, a digitised mirror image of all data for each respective turbine at any given time.

The concept of the digital twin is being heralded as a data image of the turbine enabling outmost precise turbine behaviour projection for simulations. This requires the digital twin to not only consist of large quantities of accurate sensor measurements, but also of base and context information of the actual turbine. Provision of such data can take place through a digital lifecycle log.

The digital lifecycle log therefore enables continuous information management between all stakeholders over the whole life span of a turbine. It thusly delivers major support services, such as quality- and risk management, but also the potential to rapidly locate and address a fault through better documentation.

Risks of digitalisation

Our energy supply network is already relying on the functionality of information and communication technology to a great extent. This problem is exacerbated through increasing levels of digitalisation, especially as challenges of data security surge. Because modern societies depend on energy supply, cyber attacks could potentially bring public life to a stand-still and halt essential services. Therefore, high levels of cyber security are essential for digitalised energy systems. In order to rise to that challenge, 4initia is about to complete implementation of an information management system and certification after the international norm ISO 27001 for information security and data protection.

Conclusion

Big data, machine learning, condition monitoring or digital twin are trending terms, particularly in wind energy. As outlined above, this is justified through the promise of increased technical and economical efficiency. The basis for success in the data driven energy systems, and eventually markets of our future, is the continued value-added utilisation of the increasing data load. In the case of wind energy, few steps have been taken, yet we have much further to go.

By: Tim Wehrenberg

References:

(1) International Data Corporation (2018). Data Age 2025. The Digitization of the World. From Edge to Core.
(2) ETIPWind – European Technology and Innovation Platform on Wind Energy (2016). When wind goes digital.
(3) BWE – Bundesverband WindEnergie. BWE-Standard: Schnittstellendefinition zur Nichtverfügbarkeitsmeldungen in der Direktvermarktung. Available at:
https://www.wind-energie.de/themen/anlagentechnik/betrieb/schnittstelle/ (accessed on 24 March 2020).
(4) 50Hertz Transmission, Amprion, Tennet TSO, TransnetBW (2018). Umsetzung der Vorgaben der System Operation Guideline (SO GL) zum Datenaustausch in Deutschland. Konsultationsdokument zum Datenbedarf.
(5) Bundesministerium für Bildung und Forschung. Der intelligente Windpark. Available at: https://www.bmbf.de/de/der-intelligente-windpark-10174.html (accessed on 24 March 2020).

For further reading:

1. Incite – Innovative controls for renewable source integration into smart energy systems. The wind is blowing towards a digital future. Available at:
http://www.incite-itn.eu/blog/the-wind-is-blowing-towards-a-digital-future/ (accessed on 23 March 2020).
2. ETIPWind – European Technology and Innovation Platform on Wind Energy (2016). Strategic research and innovation agenda 2018.
wind-turbine.com GmbH. Condition Monitoring Systeme.
Available at:
https://wind-turbine.com/magazin/ratgeber/betriebsfuehrung/40196/condition-monitoring-systeme.html (accessed on 23 March 2020).
3. Adler, S. u. Schmidt, J. (2019). Die digitale Lebenslaufakte – Stand der Normung. Leipzig.
4. Vogel Communications Group GmbH & Co. KG. Lebenslaufakte zeigt 1:1-Abbild von Maschinen und Anlagen.
https://www.konstruktionspraxis.vogel.de/lebenslaufakte-zeigt-11-abbild-von-maschinen-und-anlagen-a-624478/ (accessed on 23.03.2020).
5. BEE – Bundesverband Erneuerbare Energie e. V. (2019). Smarte Sektorenkopplung, Digitalisierung und Distributed Ledger Technologien.

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