The design of an offshore wind turbine (OWT) founded on a monopile foundation is principally based on dimensioning criteria related to its fundamental frequencies. These frequencies must remain outside the excitation frequencies to avoid resonance. For the calculation of the OWT natural frequencies, several studies exist, but few of them simultaneously consider both the real geometrical configuration of the OWT superstructure (tower, blades, and transition piece) and the three‐dimensional (3D) soil domain and its interaction with the monopile foundation. This paper aims at filling this gap. A rigorous 3D finite element method‐based model of a 10 MW DTU OWT installed in sand is developed. The aim is to perform a structural modal analysis of the wind turbine in parked condition. The obtained natural frequencies are compared with those corresponding to other simplified models available in literature for the foundation and the superstructure in the scope of giving an insight about how poorly the existing simplified models can predict the OWT natural frequencies. Finally, a parametric analysis is performed to study the effect of the water depth, the monopile dimensions (diameter, thickness, and embedded depth), the transition piece height, and the sandy soil relative density on the system natural frequencies.

Standalone power-supply systems become a key solution to effectively address the load demand in remote locations, wherein the voltage asymmetry raises as a particular concern in view of a large number of single-phase household and communities loads. Over these loading conditions, the use of the four-leg voltage source inverters (FL-VSIs) appears as a suitable topology to provide clean power with symmetrical voltage waveforms. The reported work employs the differential flatness theory associated with grey wolf optimization in order to guarantee proper operations of the FL-VSIs in different loading conditions. In fact, the differential flatness theory is applied to check the flatness of the FL-VSIs, which allows implementing a reduced control model. Then, a grey wolf algorithm, which acts as a tracking controller [grey wolf controller (GWC)], is developed. The GWC generates an optimal control signal used by the differentially flat model so as to ensure adequate control performance, even under severe load disturbances and model inaccuracies. A comprehensive experimental test is carried out to validate the effectiveness of the proposed flatness-based grey wolf controller.

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.