Wenxian Yang

Cost-Effective Condition Monitoring for Wind Turbines

Cost-effective wind turbine (WT) condition monitoring assumes more importance as turbine sizes increase and they are placed in more remote locations, for example, offshore. Conventional condition monitoring techniques, such as vibration, lubrication oil, and generator current signal analysis, require the deployment of a variety of sensors and computationally intensive analysis techniques. This paper describes a WT condition monitoring technique that uses the generator output power and rotational speed to derive a fault detection signal. The detection algorithm uses a continuous-wavelet-transform-based adaptive filter to track the energy in the prescribed time-varying fault-related frequency bands in the power signal. The central frequency of the filter is controlled by the generator speed, and the filter bandwidth is adapted to the speed fluctuation. Using this technique, fault features can be extracted, with low calculation times, from direct- or indirect-drive fixed- or variable-speed WTs. The proposed technique has been validated experimentally on a WT drive train test rig. A synchronous or induction generator was successively installed on the test rig, and both mechanical and electrical fault like perturbations were successfully detected when applied to the test rig.

Condition Monitoring of the Power Output of Wind Turbine Generators Using Wavelets

With an increasing number of wind turbines being erected offshore, there is a need for cost-effective, predictive, and proactive maintenance. A large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox. One way of assessing impending problems is to install vibration sensors in key positions on these subassemblies. Such equipment can be costly and requires sophisticated software for analysis of the data. An alternative approach, which does not require extra sensors, is investigated in this paper. This involves monitoring the power output of a variable-speed wind turbine generator and processing the data using a wavelet in order to extract the strength of particular frequency components, characteristic of faults. This has been done for doubly fed induction generators (DFIGs), commonly used in modern variable-speed wind turbines. The technique is first validated on a test rig under controlled fault conditions and then is applied to two operational wind turbine DFIGs where generator shaft misalignment was detected. For one of these turbines, the technique detected a problem 3 months before a bearing failure was recorded.

Development of an advanced noise reduction method for vibration analysis based on singular value decomposition

The paper developed a reasonable and practical method for identifying the useful information from the signal that has been contaminated by noise, so that to provide a feasible tool for vibration analysis. A new concept namely the Singular Entropy (SE) was proposed based on the singular value decomposition technique. With the aid of the SE, a series of investigations were done for discovering the distribution characteristics of noise contaminated and pure signals, and consequently an advanced noise reduction method was developed. The experiments showed that the proposed method was not only applied for dealing with the stationary signals but also applied for dealing with the non-stationary signals.

Detecting impulses in mechanical signals by wavelets

The presence of periodical or nonperiodical impulses in vibration signals often indicates the occurrence of machine faults. This knowledge is applied to the fault diagnosis of such machines as engines, gearboxes, rolling element bearings, and so on. The development of an effective impulse detection technique is necessary and significant for evaluating the working condition of these machines, diagnosing their malfunctions, and keeping them running normally over prolong periods. With the aid of wavelet transforms, a wavelet-based envelope analysis method is proposed. In order to suppress any undesired information and highlight the features of interest, an improved soft threshold method has been designed so that the inspected signal is analyzed in a more exact way. Furthermore, an impulse detection technique is developed based on the aforementioned methods. The effectiveness of the proposed technique on the extraction of impulsive features of mechanical signals has been proved by both simulated and practical experiments.

Wind turbine condition monitoring and fault diagnosis using both mechanical and electrical signatures

Some large wind turbines use a synchronous generator, directly-coupled to the turbine, and a fully rated converter to transform power from the turbine to the mains. This paper considers condition monitoring and diagnosis of mechanical and electrical faults in such a variable speed machine. A new condition monitoring technique is proposed in this paper, which removes the negative influence of variable wind in machine condition monitoring. This technique has a versatile function, able to detect both the mechanical and electrical faults in the wind turbine. Its effectiveness is validated by the experiments on a wind turbine condition monitoring test rig. Furthermore, a potential approach for diagnosing wind turbine drive-train mechanical faults using wind turbine generator electrical signals is introduced. The diagnosis of rotor imbalance in the wind turbine will be used as an illustrative example, heralding the detection of wind turbine electromechanical faults by power analysis. The paper offers a simpler and cheaper condition monitoring and fault diagnosis system for wind turbines.

Wind turbine condition monitoring and reliability analysis by SCADA information

As the deployment of onshore wind turbines becomes established and with an increasing deployment of offshore wind turbines, to ensure a high reliability and availability of these machines has become the focus of interest within the academic and industrial communities. One of the approaches to meet this requirement is to develop a Reliability Centered Maintenance (RCM) strategy with the aid of an advanced condition monitoring and reliability prediction system. The aim of this paper is to describe the basic idea of how wind farm Supervisory Control and Data Acquisition (SCADA) system contributes to establishing such a strategy. Examples have been given in the paper for providing a clear explanation of the opinions.

Wind Turbine Condition Monitoring Based on an Improved Spline-Kernelled Chirplet Transform

The time-varying operational conditions applied to wind turbines (WTs) not only challenge their operation but also make condition monitoring (CM) difficult. To achieve a reliable CM result, more advanced signal processing techniques, rather than the conventional spectral analyses, are urgently needed for interpreting the nonlinear and nonstationary (NNS) CM signals collected from the turbines. The work presented in this paper is an effort to meet such a requirement. Based on the proven capability of the spline-kernelled chirplet transform (SCT) in detecting the instantaneous frequencies within NNS monocomponent signals, this paper improves the SCT to enable it to detect the instantaneous amplitude of lengthy NNS multicomponent signals at a fault-related frequency of interest. The improved SCT is then applied for developing a new real-time CM technique dedicated to extracting fault-related features from WT CM signals. Experiment proves that the improved SCT has overcome existing SCT issues and is capable of correctly tracking the amplitude characteristics of NNS multicomponent signals at fault-related frequencies of interest. The new CM technique developed, based on this improved SCT, shows success in detecting both mechanical and electrical faults occurring in a WT drive train, despite the constantly varying operational conditions of the turbine. Moreover, its algorithm is efficient in computation, which not only enables it to deal with lengthy NNS CM signals but also makes it ideal for online use.

An Advanced Strategy for Detecting Impulses in Mechanical Signals

The appearance of overlapping in the results derived by continuous wavelet transform (CWT) smears the spectral features and makes the results difficult to interpret. This will significantly affect the accuracy of analysis of anomalous signals. Aiming at minimizing the undesired effect of overlapping, a new soft-thresholding method in terms of exponential functions is proposed. Using the proposed soft-threshold and combining with Donoho’s approach for reducing the structures induced by noise, a strategy for purifying the results derived by the CWT is designed. A series of simulated and practical experiments show that, after adopting the proposed strategy, the results of CWT are further purified and thereby the spectral features of the inspected signal become more explicit and much more easily identified. fDOI: 10.1115/1.1888590g

Condition Monitoring and Damage Location of Wind Turbine Blades by Frequency Response Transmissibility Analysis

Incipient defects occurring in long wind turbine (WT) blades are difficult to detect using the existing condition monitoring (CM) techniques. To tackle this issue, a new WT blade CM method is studied in this paper with the aid of the concept of transmissibility of frequency response functions. Different from the existing CM techniques that judge the health condition of a blade by interpreting individual CM signals, the proposed method jointly utilizes the CM signals measured by a number of neighboring sensors. This offers the proposed technique a unique capability of both damage detection and location. The proposed technique has been experimentally verified by using the real CM data collected during the fatigue and static tests of a full-scale WT blade. The experiment has shown that the new technique is effective not only in damage detection but also in damage location when either fiber Bragg grating strain gauges or accelerometers are used for data acquisition.

Research on a simple, cheap but globally effective condition monitoring technique for wind turbines

Vibration measurement and lubrication oil analysis are used in wind turbines (WT) as condition monitoring systems (CMS). However, they do not provide a complete solution to the WT CMS problem. The former measurement is sophisticated with high hardware costs, suffering from spurious alarms; the latter monitors the wear and fatigue of gears and bearings, but cannot detect electrical abnormalities occurring in the WT generator and electrical system. So, a simpler, cheaper but moreover globally comprehensive WT CMS is still needed, especially if the WTs are to go offshore, where they are confronted with higher risks and difficulties of access. To meet this requirement, a new WT condition monitoring technique has been researched in this paper. As the WT operates over a widely varying power range, dependant on the stochastic variations of the wind, the monitoring signals are usually non-stationary. In view of this, a wavelet-based adaptive filter is designed to extract the power energy at prescribed, fault-related frequencies which vary with time. The energy information obtained is then used as an indicator of WT condition. The central frequency of the filter is adaptive to the average rotational speed of the generator, and the filter bandwidth depends upon the fluctuation of wind speed. By using this filter, fault features can be extracted whether the WT runs at fixed or variable speed. The proposed technique has been experimentally validated on a WT Test Rig using both synchronous and induction generators as exemplars. Experiments prove that the proposed technique is efficient in assessing the WT condition for both mechanical and electrical abnormalities.