Fault Diagnosis of the Wind Turbine Main Bearing through Multifractal Theory

2013 ◽  
Vol 644 ◽  
pp. 337-340
Author(s):  
Quan Gu ◽  
Chang Zheng Chen ◽  
Xiang Jun Kong ◽  
Xian Ming Sun ◽  
Bo Zhou ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Large Scale ◽  
Spectral Characteristics ◽  
Multifractal Spectrum ◽  
Hölder Exponent ◽  
Main Bearing ◽  
Vibration Signals ◽  
Fault Features ◽  
Multifractal Theory ◽  
Incipient Fault

Because the vibration signals of faulty wind turbine are non-linear and non-stationary, to obtain the obvious fault features become difficult. In this study, the incipient fault of the main bearing used in large scale wind turbine is studied by using a multifractal method based on the Wavelet Modulus Maxima (WTMM) method. The real vibration signals from the main bearings are analyzed using the multifractal spectrum. The spectrum of the vibration signals is quantified by spectral characteristics including its range and the Hölder exponent corresponding to the maximum dimension. The results show that the range of Hölder exponent of the main bearing which worked normally is much narrower. While the ranges of the vibration signals of the main bearing with incipient fault are wider. We also found that the fault features are different at various wind turbine rotational frequencies. Those demonstrate that the incipient fault features of main bearing of large scale wind turbine can be extract effectively using the multifractal spectrum obtained from WTMM method.

Wind Turbine Generator Fault Detection by Wavelet-Based Multifractal Analysis

2013 ◽  
Vol 644 ◽  
pp. 346-349
Author(s):  
Chang Zheng Chen ◽  
Yu Zhang ◽  
Quan Gu ◽  
Yan Ling Gu
Keyword(s):  
Wind Turbine ◽  
Multifractal Analysis ◽  
Vibration Signal ◽  
Rotational Frequency ◽  
Turbine Generator ◽  
Vibration Signals ◽  
Wind Turbine Generator ◽  
Fault Features ◽  
Multifractal Theory ◽  
Incipient Fault

It is difficult to obtain the obvious fault features of wind turbine, because the vibration signal of them are non-linear and non-stationary. To solve the problem, a multifractal analysis based on wavelet is presented in this research. The real signals of 1.5 MW wind turbine are studied by multifractal theory. The incipient fault features are extracted from the original signal. Using the Wavelet Transform Modulo Maxima Method, the multifractal was obtained. The results show that fault features of high rotational frequency of wind turbine are different from low rotational frequency, and the complexity of the vibration signals increases with the rotational frequency. These demonstrate the multifractal analysis is effective to extract the fault features of wind turbine generator.

Wind Turbine Gearbox Fault Detection Based on Multifractal Analysis

2013 ◽  
Vol 644 ◽  
pp. 312-316
Author(s):  
Chang Zheng Chen ◽  
Ping Ping Pan ◽  
Qiang Meng ◽  
Yan Ling Gu
Keyword(s):  
Wind Turbine ◽  
Structural Vibrations ◽  
Wind Turbine Gearbox ◽  
Vibration Signals ◽  
The Real ◽  
Signal Processing Method ◽  
Fault Features ◽  
Incipient Faults ◽  
Weak Fault ◽  
Multifractal Method

The presence of irregularity in periodical vibration signals usually indicates the occurrence of wind turbine gearbox faults. Unfortunately, detecting the incipient faults is a difficult job because they are rather weak and often interfered by heavy noise and higher level macro-structural vibrations. Therefore, a proper signal processing method is necessary. We used the wavelet-based multifractal method to extract the impulsive features buried in noisy vibration signals. We first calculated the wavelet transform modulo maxima lines from the real vibration signals, then, obtained the singularity spectrum from the lines. The analysis results of the real signals showed that the proposed method can effectively extract weak fault features.

Passive Structural Health Monitoring Applied to a Wind Turbine Model

2009 ◽  
Author(s):  
Kyle Bassett ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Large Scale ◽  
Residual Error ◽  
Series Data ◽  
Vibration Signals ◽  
Structural Health ◽  
Monitoring Technique

Structural health monitoring is a technique devised to monitor the structural conditions of a system in an attempt to take corrective measures before the system fails. A passive structural health monitoring technique is presented, which serves to leverage historic time series data in order to both detect and localize damage on a wind turbine blade aerodynamic model. First, vibration signals from the healthy system are recorded for various input conditions. The data is normalized and auto-regressive (AR) coefficients are determined in order to uniquely identify the normal behavior of the system for each input condition. This data is then stored in a healthy state database. When the structural condition of the system is unknown the vibration signals are acquired, normalized and identified by their AR coefficients. Damage is detected through the residual error which is calculated as the difference between the AR coefficients of the unknown and healthy structural conditions. This technique is tailored for wind turbines and the application of this approach is demonstrated in a wind tunnel using a small turbine blade held with four springs to create a dual degree-of-freedom system. The vibration signals from this system are characterized by free-stream speed. Damage is replicated through mass addition on each of the blades ends and is located by an increase in residual error from the accelerometer mounted closest to the damaged area. The outlined procedure and demonstration illustrate a single stage structural health monitoring technique that, when applied on a large scale, can avoid catastrophic turbine disasters and work to effectively reduce the maintenance costs and downtime of wind farm operations.

scholarly journals Multifractal Characteristics Analysis Based on Slope Distribution Probability in the Yellow River Basin, China

2021 ◽  
Vol 10 (5) ◽  
pp. 337
Author(s):  
Zilong Qin ◽  
Jinxin Wang ◽  
Yan Lu
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Yellow River ◽  
Multifractal Spectrum ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Distribution Probability ◽  
Multifractal Theory ◽  
The Yellow River Basin ◽  
Geomorphological Features

Multifractal theory provides a reliable method for the scientific quantification of the geomorphological features of basins. However, most of the existing research has investigated small and medium-sized basins rather than complex and large basins. In this study, the Yellow River Basin and its sub-basins were selected as the research areas, and the generalized fractal dimension and multifractal spectrum were computed and analyzed with a multifractal technique based on the slope distribution probability. The results showed that the Yellow River Basin and its sub-basins exhibit clear multifractal characteristics, which indicates that the multifractal theory can be applied well to the analysis of large-scale basin geomorphological features. We also concluded that the region with the most uneven terrain is the Yellow River Downstream Basin with the “overhanging river”, followed by the Weihe River Basin, the Yellow River Mainstream Basin, and the Fenhe River Basin. Multifractal analysis can reflect the geomorphological feature information of the basins comprehensively with the generalized fractal dimension and the multifractal spectrum. There is a strong correlation between some common topographic parameters and multifractal parameters, and the correlation coefficients between them are greater than 0.8. The results provide a scientific basis for analyzing the geomorphic characteristics of large-scale basins and for the further research of the morphogenesis of the forms.

scholarly journals Complete Teager Energy Operator: A Novel Method for Enhancing Micro-impact Signal

2021 ◽  
Author(s):  
Xu Dong ◽  
huipeng li
Keyword(s):  
Large Scale ◽  
Energy Operator ◽  
Original Method ◽  
Data Sets ◽  
Teager Energy Operator ◽  
Fault Features ◽  
Novel Method ◽  
The Common ◽  
Incipient Fault ◽  
Teager Energy

Abstract The output of conventional Teager energy operator (TEO) is approximately equal to the square product of the instantaneous amplitude and the instantaneous frequency ( A 2 Ω 2 ). The original TEO can effectively enhance the transient shock components and suppress the non-impacting elements, and it also changes the frequency distribution of the original shock. In this paper, a complete Teager energy operator is proposed, and its expression is more exact than original method. By keeping the positive and negative distribution of the shock signal x ( t ), the fundamental frequency energy of the impulses can be effectively enhanced. The incipient fault characteristics of large-scale rotating machinery are typically micro shock pulse, extremely weak and mixed with heavy noise. Preprocessing the fault signal and enhancing the micro shock component are essential means to extract the early fault features. In the experiment part, the applicability of the proposed method is verified by the simulated micro impact signal, the common bearing fault data-sets and the practical measured data of the test bench.

Wavelet-based multifractal analysis of large scale wind turbine main bearing

2013 ◽  
Vol 5 (1) ◽  
pp. 013102 ◽  
Author(s):  
Changzheng Chen ◽  
Xianming Sun ◽  
Quan Gu ◽  
Bo Zhou ◽  
Yanling Gu
Keyword(s):  
Wind Turbine ◽  
Multifractal Analysis ◽  
Large Scale ◽  
Main Bearing

scholarly journals Health monitoring of Bridges based on multifractal theory

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ling Zhao ◽  
Jiawei Ding ◽  
Haiming Liu
Keyword(s):  
Health Monitoring ◽  
Multifractal Spectrum ◽  
Box Dimension ◽  
Health State ◽  
Nonlinear Characteristics ◽  
Bridge Health Monitoring ◽  
Fault Features ◽  
Multifractal Theory ◽  
Fault State ◽  
Multifractal Method

Abstract The multifractal theory is applied in an analysis of bridge disturbance signals with the aim of investigating their nonlinear characteristics, and then the recognisable fault features are extracted from them. By calculating the box dimension and correlation dimension of the bridge disturbance signal, the dimensional characteristics of the disturbance data are analysed to distinguish the health-state of the bridge. Finally, taking the bridge disturbance data as an example, and by using the multifractal spectrum analysis of the disturbance data, it is concluded that the multifractal method can accurately identify the fault state and realise the bridge health monitoring.

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

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