Investigation of Dynamic Loads in Wind Turbine Drive Trains Due to Grid and Power Converter Faults

Electrical faults can lead to transient and dynamic excitations of the electromagnetic generator torque in wind turbines. The fast changes in the generator torque lead to load oscillations and rapid changes in the speed of rotation. The combination of dynamic load reversals and changing rotational speeds can be detrimental to gearbox components. This paper shows, via simulation, that the smearing risk increases due to the electrical faults for cylindrical roller bearings on the high speed shaft of a wind turbine research nacelle. A grid fault was examined for the research nacelle with a doubly fed induction generator concept. Furthermore, a converter fault was analyzed for the full size converter concept. Both wind turbine grid connection concepts used the same mechanical drive train. Thus, the mechanical component loading was comparable. During the grid fault, the risk of smearing increased momentarily by a maximum of around 1.8 times. During the converter fault, the risk of smearing increased by around 4.9 times. Subsequently, electrical faults increased the risk of damage to the wind turbine gearbox bearings, especially on the high speed stage.

Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform and Its Application in Wind Turbine Gearbox Fault Diagnosis

Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing Time-Frequency (TF) Analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated Time-Frequency Representation (TFR), this paper proposes a new TFA method, namely Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical Continuous Wavelet Transform (CWT), and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus Adaptive Wavelet Transform (AWT) is generated. Then, on the basis of the theoretical framework of Synchrosqueezing Transform (SST) and accurate Instantaneous Frequency (IF) estimation by the utilization of second-order local demodulation operator, Adaptive Second-order Synchrosqueezing Wavelet Transform (AWSST2) is formed. Considering that the quality of actual time-frequency analysis is greatly disturbed by noise components, through performing multiple Synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since Synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

Analysis and Research on Fracture Cause of Fixed Shaft of Torsion Arm of Wind Turbine Gearbox

In a wind power plant of a wind power plant limited liability company, the fixed shaft of the torsion arm of the gear box broke during the operation of a wind power generator set. In order to find out the cause of fracture, the fracture fixed shaft of torsion arm was comprehensively detected and analyzed by means of appearance morphology analysis, chemical composition analysis, mechanical properties testing, microstructure testing and fracture micro-area analysis. The results show that the main reasons for the fracture of the fixed shaft of the torsion arm of the fan gear box are as follows: improper heat treatment process of the fixed shaft of the torsion arm of the gear box causes a large amount of massive ferrite in the material structure, resulting in insufficient strength of the material; the inclusion in the material is serious, resulting in unqualified impact toughness; Shaft surface surfacing Cr - Mn stainless steel material causes the fusion zone C migration form brittle layer, at the same time the vast difference between the state of welding layer and substrate organization fusion zone caused by larger remnants stress, the embrittlement in bond layer to form the intergranular crack crack source, and in the process of the equipment operation under the action of cyclic torsional and impact load, Cracks propagate in a fatiguing manner and lead to eventual fracture.

Application of Generalized Composite Multiscale Lempel–Ziv Complexity in Identifying Wind Turbine Gearbox Faults

Wind turbine gearboxes operate in harsh environments; therefore, the resulting gear vibration signal has characteristics of strong nonlinearity, is non-stationary, and has a low signal-to-noise ratio, which indicates that it is difficult to identify wind turbine gearbox faults effectively by the traditional methods. To solve this problem, this paper proposes a new fault diagnosis method for wind turbine gearboxes based on generalized composite multiscale Lempel–Ziv complexity (GCMLZC). Within the proposed method, an effective technique named multiscale morphological-hat convolution operator (MHCO) is firstly presented to remove the noise interference information of the original gear vibration signal. Then, the GCMLZC of the filtered signal was calculated to extract gear fault features. Finally, the extracted fault features were input into softmax classifier for automatically identifying different health conditions of wind turbine gearboxes. The effectiveness of the proposed method was validated by the experimental and engineering data analysis. The results of the analysis indicate that the proposed method can identify accurately different gear health conditions. Moreover, the identification accuracy of the proposed method is higher than that of traditional multiscale Lempel–Ziv complexity (MLZC) and several representative multiscale entropies (e.g., multiscale dispersion entropy (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).