The online optimisation of stator vane settings in multi-stage axial compressors

Abstract

Axial compressors for high efficiency industrial gas turbines are required to operate over a wide range of mass flow rates and rotational speeds. However, the useful range of operation of the axial-flow compressor is limited by the onset of two instabilities known as surge and rotating stall. To resolve these problems, variable stator blades or VGV's are considered by optimising the blade setting in order to avoid the stall and subsequent surge. A steady state model of a 15 stage multi-stage axial compressor is utilised here to investigate the performance, particularly for obtaining acceptable optimisation convergence time for practical purposes. For the effective search for an optimum setting, the variation in VGV's with respect to a different combination of objective functions is considered. In this paper, self-tuning extremum control and a particle swarm optimisation method are proposed and implemented to obtain the best value for a normalised objective function. The results demonstrate the relative effectiveness of the two algorithms and the suitability for their use in this proposed application. The study clearly demonstrates that the PSO provides the best performance in seeking the optimum of the chosen objective functions.