Signal Processing Techniques to Detect Centrifugal Compressors Instabilities in Large Volume Power Plants

The present energy worldwide scenario requires energy plants working far from their on-design conditions: this causes transient operations and partial load working conditions to happen frequently. For dynamic compressors, this may cause them operating close to their instability region, especially during load and velocity transients or emergency shutdowns. Moreover, in recent years the need of improving global efficiency of energy plants has led to the success of advanced cycles, such as fuel cells hybrid systems, based on micro Gas Turbines (mGT) technology. This is an important issue for centrifugal compressors, which are normally employed in this kind of systems, since now they have to face higher volumes in comparison to traditional combined cycle plants.

The present paper shows signal processing techniques applied to experimental data obtained from a T100 microturbine connected with different volume sizes. This experimental activity was carried out with the test rig developed at the University of Genoa for hybrid system emulation. However, these results can be extended to all the advanced cycles in which a microturbine is connected with an additional external component responsible for volume size increase. Since in this case a 100 kW microturbine was used, the volume was located between the recuperator outlet and the combustor inlet like in the typical cases related to small size plants. A modular vessel was used to perform and to compare the tests with different volume sizes.

The results here reported are related to rotating stall and surge operations. This analysis was carried out to extend the knowledge about these risk conditions: the systems equipped with large volume size connected to the machine present critical issues related to surge and stall prevention, especially during transient operations towards low mass flow rate working conditions.  

The main expressions of instability of compression systems are indeed rotating stall and surge, which respectively involve the compressor itself and the whole energy system in which the machine is placed. Both mild and deep surge, indeed, are preceded and accompanied by rotating stall. This phenomenon is characterized by stalled fluid cells that rotate slower than the surrounding medium does; nevertheless, it is still a stable condition in which most centrifugal compressors do work, but it causes a remarkable decrease of performances of the machine together with induced vibrations due to an anomalous pressure distribution along the circumferential direction, which results in an unbalancing force on the rotor, instantaneously varying both in nodulous and direction. For these reasons, the system was equipped with different dynamic probes to measure the vibroacoustic operational response during normal and anomalous operations.

Investigations based on acoustic and vibrational measurements appear to provide an interesting diagnostic and predictive solution by adopting suitable quantifiers calculated from microphone and accelerometer signals. Hence different possible tools for rotating stall and incipient surge identification have been developed through the use of different signal processing techniques, such as wavelet analysis and the combination of cyclostationary and Higher Order Statistics Analysis (HOSA) methods. Indeed, these advanced techniques are necessary to maximize all the information conveyed by acquired signals, particularly in those environments in which measured physical quantities are hidden by strong noise, including both broadband background one (i.e. typical random noise) but also uninteresting components associated to the signal of interest. For instance, in complex coupled physical systems like the one we mean to study, which do not satisfy the hypothesis of linear and Gaussian processes inside them, it is reasonable to exploit these kinds of tools, instead of the classical Fast Fourier Transform (FFT) technique by itself, which is mainly adapt for linear systems periodic analysis.