The optimization of the operations of electrical machines is a topic of paramount importance when considering energy efficiency of industrial plants. They are responsible for around 45% of the total global electric power consumption, thus any improvement of their efficiency can result to be extremely beneficial in terms of power savings and emissions reduction.
Electrical machines are operated at variable speed when the electric supply passes through a converter. This lets one modify the voltage frequency and amplitude, hence operate the machine at variable torque and speed. The machine and its converter are grouped together in the electrical drive.
The main topic of Work Packet 4 is to make energy savings for optimal control of electrical drives, in particular considering a compressor as load. In particular we consider nonlinear models for the losses which occur in the motor. These are normally not accounted for in the state-of-the-art, since the constructor provides only limited information and they cannot be easily estimated.
Optimizing over these sources of losses turns out to be beneficial, especially when compared to standard techniques. Hence, we obtain steady-state efficiency improvements of 2 % when considering optimal techniques compared to the state-of-the-art, without reducing the delivered torque and speed on the rotor shaft. The results have been experimentally assessed on the test bench in figure.
Optimization can also be efficiently employed to control compressors in order to avoid instabilities and maximize their efficiency. This is done by Model Predictive Control, which predicts the future evolution of the model and considers input and state constraints of the system. In this way, phenomena as surge are avoided and a desired operating condition is tracked. This prevents industrial compression system from being stopped when the operating conditions are not ideal, and extends the set of feasible operating points of the compression system.