Fault Diagnosis of a Permanent Magnet Synchronous Generator Wind Turbine

Khodakaramzadeh, S.; Ayati, M.; Haeri Yazdi, M. R.

doi:10.22061/jecei.2020.7424.408

Document Type : Original Research Paper

Authors

School of Mechanical Engineering, University of Tehran, Tehran, Iran

https://doi.org/10.22061/jecei.2020.7424.408

Abstract

Background and Objectives: Designing a terminal sliding mode observer (TSMO) in order to estimate the potential faults in a wind turbine with a doubly fed induction generator (DFIG) has been studied in previous research works. In this paper, a method for fault detection of a permanent magnet synchronous generator (PMSG) wind turbine using a TSMO is developed.
Methods: The wind turbine (WT) dynamic model including, blades, drive train, PMSG, maximum power capture controller, and pitch controller is linearized around its equilibrium point and is simulated in MATLAB Simulink. A PID controller is designed for capturing the maximum power from wind. Also, a PI controller is designed in order to control the pitch angle. In this research, the blade imbalance fault (BIF), which is due to the difference between turbine blades’ mass distribution, is investigated. This fault may happen over time and causes rotor mass imbalance that leads to vibrations in the generator’s shaft rotating speed. A fault detection system (FDS) is proposed using a terminal sliding mode observer in order to diagnose the BIF.
Results: Using the designed TSMO, the estimation errors of not only measured states but also unmeasured states converge to zero in finite time. This leads to the fast action of the FDS before a failure happens. Using the proposed FDS, the states and fault are estimated such that the estimation errors of states and the fault converge to zero in 0.035 seconds.
Conclusion: The convergence of state estimation errors to zero in finite time, which is verified by simulation results, satisfies the authors’ expectation that using TSMO, the estimation errors of both output and non-output states converge to zero in finite time.

Keywords

20.1001.1.23223952.2021.9.2.2.0

Main Subjects

Fault Detection Algorithms

Open Access

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Shahid Rajaee Teacher Training University

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Journal of Electrical and Computer Engineering Innovations (JECEI)

Fault Diagnosis of a Permanent Magnet Synchronous Generator Wind Turbine

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Volume 9, Issue 2
July 2021
Pages 143-152

Fault Diagnosis of a Permanent Magnet Synchronous Generator Wind Turbine

References

References

Send comment about this article

Volume 9, Issue 2July 2021Pages 143-152

Volume 9, Issue 2
July 2021
Pages 143-152