Cost-Effective Condition Monitoring for Wind Turbines

By Wenxian Yang, P. Tavner, C. Crabtree, M. Wilkinson. IEEE transactions on industrial electronics (1982. Print), 2010

369 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4134af6f3cefed969dd3dee05d7828630004b790

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Mohamed El Hachemi Benbouzid
2. Vincent Choqueuse
3. Yassine Amirat

In Control and Systems Engineering

1. David Flynn
2. Mohamed El Hachemi Benbouzid
3. Vincent Choqueuse
4. Valentin Robu
5. Yassine Amirat

In Electrical and Electronic Engineering

1. David Flynn
2. Mike Barnes
3. Mohamed El Hachemi Benbouzid
4. Valentin Robu

Citations in the corpus

• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, neutral): “conditions [29].”
• From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis (2013, neutral): “I NDUSTRIAL systems have been becoming more complex and expensive with less tolerance for performance degradation, productivity decrease, and safety hazards, such as wind farms [28], [98], aircraft engines [20], [53], petrochemical pro-”
• Machine learning methods for wind turbine condition monitoring: A review (2019, methodology): “…[51,55,56], Morlet Wavelet feature extraction [57] and Wigner-Ville distribution [58], SVD [59], cluster-based Wavelet feature extraction [60], Multi-Wavelet feature extraction from vibration data [61], energy tracking [62] and with S-transform [63], instantaneous power spectrum [64], etc.”
• EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component (2013, methodology): “These sensors are managed together in different architectures and coupled with algorithms to allow an efficient monitoring of the system condition [3].”