Ice accretion on wind turbines' blades is one of the main challenges of systems installed in cold climate locations, resulting in power performance deterioration and excessive nacelle oscillation. In this work, consistent detection of icing events is achieved utilizing indications from the nacelle accelerometers and power performance analysis. Features extracted from these two techniques serve as inputs in a decision-making scheme, allowing early activation of de-icing systems or shut down of the wind turbine. An additional parameter is the month of operation, assuring consistent outcomes in both winter and summer seasons. The amplitude of lateral nacelle vibration at rotor speed is the used condition indicator from vibration standpoint, which is verified by the presence of sinusoidal shape in high-resolution time waveforms. Employment of k-nearest neighbour on wind speed -power production data sets leads to successful recognition of power performance deterioration. Results from one wind park consisting of 13 turbines operating under icing are presented, where similar patterns on both vibration and power curve data validate the effectiveness of the proposed approach on the reliable detection of icing formation.

Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

A power performance assessment technique is developed for the detection of power production discrepancies in wind turbines. The method employs a widely used nonparametric pattern recognition technique, the kernel methods. The evaluation is based on the trending of an extracted feature from the kernel matrix, called similarity index, which is introduced by the authors for the first time. The operation of the turbine and consequently the computation of the similarity indexes is classified into five power bins offering better resolution and thus more consistent root cause analysis. The accurate and proper detection of power production changes is demonstrated in cases of icing, power derating, operation under noise reduction mode, and incorrect controller input signal. Finally, overviews are illustrated for parks subjected to icing and operating under limited rotational speed. The comparison between multiple adjacent turbines contributes further to the correct evaluation of the park overall performance.

-Efficient fault detection in generators often require prior knowledge of fault behavior, which can be obtained from theoretical analysis, often carried out by using discrete models of a given generator. Mathematical models are commonly represented in the DQ0 reference frame, which is convenient in many areas of electrical machine analysis. However, for fault investigations, the phase-coordinate representation has been found more suitable. This paper presents a mathematical model in phase coordinates of the DFIG with two parallel windings per rotor phase. The model has been implemented in Matlab and its properties in context of fault simulations and investigations has been investigated. Some of the most common faults have been simulated, namely broken rotor bars or windings, dynamic eccentricities and stator phase winding short circuits. These fault conditions propagate to the stator current as undesired spectral components, which can be detected by applying frequency spectrum analysis.

Faults in planetary gears and related bearings, e.g. planet bearings and planet carrier bearings, pose inherent difficulties on their accurate and consistent detection associated mainly to the low energy in slow rotating stages and the operating complexity of planetary gearboxes. In this work, statistical features measuring the signal energy and Gaussianity are calculated from the residual signals between each pair from the first to the fifth tooth mesh frequency of the meshing process in a multi-stage wind turbine gearbox. The suggested algorithm includes resampling from time to angular domain, identification of the expected spectral signature for proper residual signal calculation and filtering of any frequency component not related to the planetary stage. Two field cases of planet carrier bearing defect and planet wheel spalling are presented and discussed, showing the efficiency of the followed approach and the possibility of characterizing a fault as localized or distributed.