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| Content Provider | IET Digital Library |
|---|---|
| Author | Zhang, Yuxian Wang, Kefeng Qian, Xiaoyi Gendeel, Mohammed |
| Abstract | A robust fault-detection design based on residual Kullback–Leibler (K–L) divergence, which is applied to a 5 MW offshore wind turbine (WT) benchmark, is presented. The main challenges of the wind turbine fault detection lie in its complex operation conditions and disturbances as well as measurement noise. For overcoming these difficulties, the measured data are divided on the basis of the operation conditions of WT. The robust residual generator based on parity vector is adopted to calculate the residual under different operation conditions. The K–L divergence based on probability density function is employed to measure the residual. Then, the threshold for the fault detection is determined in line with both false alarm rate and missed detection rate. The simulation results show that the performance and effectiveness of the proposed robust fault detection are better than compared with other data-driven fault-detection approaches. |
| Starting Page | 2400 |
| Ending Page | 2408 |
| Page Count | 9 |
| ISSN | 17521416 |
| Volume Number | 13 |
| e-ISSN | 17521424 |
| Issue Number | Issue 13, Oct (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-rpg/13/13 |
| Journal | IET Renewable Power Generation |
| Publisher Date | 2019-07-03 |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Complex Operation Condition Data-driven Fault-detection Approach Different Operation Condition Fault Diagnosis Measurement Noise Missed Detection Rate Offshore Installations Offshore Wind Turbine Benchmark Power 5.0 MW Power Generation Fault Probability Residual Kullback–Leibler Divergence Residual K–L Divergence Robust Fault Detection Robust Fault-detection Design Robust Residual Generator Statistics Wind Power Plant Wind Turbine Wind Turbine Fault Detection Lie |
| Content Type | Text |
| Resource Type | Article |
| Subject | Renewable Energy, Sustainability and the Environment |
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