CALM - CollAborating network of Large electric Machines

Collaborating networks of large electric machines

The impact of electricity consumption of electrical machines on the total energy consumption worldwide is large. Between 43%-46% of all end‐use electricity consumption is through electric motors, giving rise to around 6040 Mt of CO2 emissions per year [1]. Significant energy efficiency improvements can be achieve by introducing electrical machines with frequency converters - the so-called variable-speed drives (VSDs) – which allow for variable speed and torque based on the load and process requirements through electronic control. However, the maximum efficiency of a plant with a specific process such as pumping or fan is not necessarily obtained through pushing the efficiency of the electrical components alone. The optimization of the VSD electrical operation based on the mechanical constraints of the process is to be preferred. In other words, the most efficient solutions are those that embrace the efficiency optimization at the plant level [2]. In order to obtain an efficiency optimization at component and at system level in large electric machines, it is necessary to have an accurate modelling of the machine state, the measurement of relevant variables with ad-hoc sensors mounted on the machines and the process of the data [3]. All of this should seamlessly provide the information to the control layers responsible of maintaining the machines close to their optimal operating points without endangering their life time, as well as to operate all together within a predefined operation envelope required by the application.

The objective of this project is to design the sensing, communication and control infrastructure of a collaborating network of large electric machines, which allows a learning-based optimization of operation patterns while providing at the same time information on the machine physical state. The project will start with definition of the best methodology for the characterization of the electric machine performance, in relation to relevant control objectives and with characterization of the electric machines interaction within the plant. It will be followed by the design of a communication infrastructure that can provide provable performance guarantees for local as well as centralized processing of sensed data, under the constraints posed by the highly challenging wireless environment in rotating electric machines, and finally by the design of distributed cognitive control processes, that can continuously improve based on the experienced performance of the plant will be investigated. The project will include testbed implementation and experiments at ABB with the contribution of the PhD student and research visit at the University of Agder.

Project manager:

Viktoria Fodor

PhD student

to be recruited, Electric Power and Energy Systems

PhD student advisors:

Prof Viktoria Fodor, Communication Networks

Dr. Luca Peretti, Electric Power and Energy Systems

External supervisory team:

Dr Juliette Soulard, University of Warwick, UK
Prof. Baltasar Beferull-Lozano, University of Agder, member of Petromaks 2 [4]

References

[1] P. Waide and C. U. Brunner, "Energy-Efficiency policy opportunities for electric motor-driven systems," International Energy Agency, 2011,

[2] “Facts about the new European eco-compatible design standard EN 50598”