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Liu, Y (2018) FAULT DETECTION AND ISOLATION FOR WIND TURBINE DYNAMIC SYSTEMS. Doctoral thesis, Liverpool John Moores University.
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Abstract
This work presents two fault detection and isolation (FDI) approaches for wind turbine systems (WTS). Firstly, a non-linear mathematical model for wind turbine (WT) dynamics is developed. Based on the developed WTS mathematical model, a robust fault detection observer is designed to estimate system faults, so as to generate residuals. The observer is designed to be robust to system disturbance and sensitive to system faults. A WT blade pitch system fault, a drive-train system gearbox fault and three sensor faults are simulated to the nominal system model, and the designed observer is then to detect these faults when the system is subjected to disturbance. The simulation results showed that the simulated faults are successfully detected. In addition, a neural network (NN) method is proposed for WTS fault detection and isolation. Two radial basis function (RBF) networks are employed in this method. The first NN is used to generate the residual from system input/output data. A second NN is used as a classifier to isolate the faults. The classifier is trained to achieve the following target: the output are all “0”s for no fault case; while the output is “1” if the corresponding fault occurs. The performance of the developed neural network FDI method was evaluated using the simulated three sensor faults. The simulation results demonstrated these faults are successfully detected and isolated by the NN classifier.
| Item Type: | Thesis (Doctoral) |
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| Uncontrolled Keywords: | Wind Turbine; Fault Detection and Isolation; FDI |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics. Nuclear engineering |
| Divisions: | Electronics & Electrical Engineering (merged with Engineering 10 Aug 20) |
| Date Deposited: | 20 Apr 2018 09:11 |
| Last Modified: | 08 Nov 2022 13:47 |
| DOI or ID number: | 10.24377/LJMU.t.00008524 |
| Supervisors: | Yu, D |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/8524 |
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