Abstract
Most electrical machines and drive signals are non-Gaussian and are highly nonlinear in nature. A useful set of techniques to examine such signals relies on higher-order statistics (HOS) spectral representations. They describe statistical dependencies of frequency components that are neglected by traditional spectral measures, namely the power spectrum (PS). One of the most used HOS is the bispectrum where examining higher-order correlations should provide further details and information about the conditions of electric machines and drives. In this context, the stator currents of electric machines are of particular interest because they are periodic, nonlinear, and cyclostationary. This current is, therefore, well adapted for analysis using bispectrum in the designing of an efficient condition monitoring method for electric machines and drives. This paper is, therefore, proposing a bispectrum-based diagnosis method dealing the with tidal stream turbine (TST) rotor blades biofouling issue, which is a marine environment natural process responsible for turbine rotor unbalance. The proposed bispectrum-based diagnosis method is verified using experimental data provided from a permanent magnet synchronous generator (PMSG)-based TST experiencing biofouling emulated by attachment on the turbine blade. Based on the achieved results, it can be concluded that the proposed diagnosis method has been very successful. Indeed, biofouling imbalance-related frequencies are clearly identified despite marine environmental nuisances (turbulences and waves).
Highlights
IntroductionEnergies 2020, 13, 2888 have been verified to work over the last few years [4,6,7,8,9,10,11,12,13]
This paper has addressed the issue of diagnosing biofouling in tidal stream turbines
Biofouling is a natural process in marine environments that is responsible for turbine rotor unbalance
Summary
Energies 2020, 13, 2888 have been verified to work over the last few years [4,6,7,8,9,10,11,12,13] In most cases, these conventional techniques do not take into account all the situations, as their application requires signal linearity and stationary hypotheses [14,15,16,17,18,19]. The main idea is that as these systems degrade, they tend to become more nonlinear, generating new frequency components [20,21,22,23,24]. The PC correlation between the two interacting frequencies and the new frequency is the well-known quadratic phase coupling (QPC) and has been known as NTI “true” signature in dynamic systems [5,7,20,25,26]
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