Due to constantly varying wind speed, wind turbine (WT) rotor and the other drive train components often operate at variable speeds in order to capture energy from wind as efficiently as possible and therefore generate more electric power. Due to the variable loads and rotational speed, the condition monitoring (CM) signals collected from WTs always contain intra-wave features, which are difficult to extract through performing conventional Time-Frequency Analysis (TFA) because the successful extraction of these intra-wave characteristics requests a locally adaptive signal processing technique. To now, only Empirical Mode Decomposition (EMD) and its extension form can meet such a requirement. However, practice has shown that the EMD and those EMD-based techniques also suffer a number of defects in TFA (e.g. weak robustness of against noise, unidentified ripples, inefficiency in detecting side-band frequencies, etc.). The existence of these issues has significantly limited the extensive application of the EMD family techniques to WT CM. Recently, an alternative TFA method, namely Variational Mode Decomposition (VMD), was proposed to overcome all these issues. The purpose of this paper is to verify the superiorities of the VMD over the EMD and investigate its potential application to the future WT CM.Experiment has shown that the VMD outperforms the EMD not only in noise robustness but also in multi-component signal decomposition, side-band detection, and intra-wave feature extraction.Thus, it has potential as a promising technique for WT CM.

Reliable condition monitoring (CM) highly relies on the correct extraction of fault-related features from CM signals. This equally applies to the CM of wind turbines (WTs). Although influenced by slowly rotating speeds and constantly varying loading, extracting fault characteristics from lengthy, nonlinear, non-stationary WT CM signals is extremely difficult, which makes WT CM one of the most challenge tasks in wind power asset management despites that lots of efforts have been spent. Attributed to the superiorities to empirical mode decomposition and its extension form Hilbert-Huang transform in dealing with nonlinear, non-stationary CM signals, the recently developed variational mode decomposition (VMD) casts a glimmer of light for the solution for this issue. However, the original proposed VMD adopts default values for both number of modes and filter frequency bandwidth. It is not adaptive to the signal being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, a precise feature extraction method based on optimised VMD is investigated. The experiments have shown that thanks to the use of the proposed optimisation strategies, the faultrelated features buried in WT CM signals have been extracted out successfully.

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We study the problem of anonymizing data with quasi-sensitive attributes. Quasi-sensitive attributes are not sensitive by themselves, but certain values or their combinations may be linked to external knowledge to reveal indirect sensitive information of an individual. We formalize the notion of l-diversity and t-closeness for quasi-sensitive attributes, which we call QS l-diversity and QS t-closeness, to prevent indirect sensitive attribute disclosure. We propose a two-phase anonymization algorithm that combines quasiidentifying value generalization and quasi-sensitive value suppression to achieve QS l-diversity and QS t-closeness.