Fault Detection and Isolation for Wind Turbine Electric Pitch System

Jiangsheng Zhu; Kuichao Ma; Amin Hajizadeh; Mohsen N. Soltani; Zhe Chen

doi:10.1109/PEDS.2017.8289226

Fault Detection and Isolation for Wind Turbine Electric Pitch System

Jiangsheng Zhu, Kuichao Ma, Amin Hajizadeh, Mohsen N. Soltani, Zhe Chen

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

5 Citations (Scopus)

Abstract

This paper presents a model-based fault detection and isolation scheme applied on electric pitch system of wind turbines. Pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be precisely detected to prevent failures and decrease downtime. To detect faults of electric pitch actuators and sensors, an extended kalman filter (EKF) based multiple model adaptive estimation (MMAE) designed to estimate the states of the system. The proposed method is demonstrated in case studies. The simulation results show that the proposed method detects different fault scenarios of wind turbines under the stochastic external condition.

Original language	English
Title of host publication	Proceedings of 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)
Number of pages	6
Publisher	IEEE Press
Publication date	Dec 2017
Pages	618-623
ISBN (Electronic)	978-1-5090-2364-6
DOIs	https://doi.org/10.1109/PEDS.2017.8289226
Publication status	Published - Dec 2017
Event	2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS) - Honolulu, United States Duration: 12 Dec 2017 → 15 Dec 2017

Conference

Conference	2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)
Country/Territory	United States
City	Honolulu
Period	12/12/2017 → 15/12/2017

Series	IEEE International Conference on Power Electronics and Drive Systems
ISSN	2164-5264

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10.1109/PEDS.2017.8289226

https://ieee-peds-2017.com/wp-content/uploads/2017/11/391.pdf

5 Citations
1 Ph.D. thesis

Wind Farm Control under Generator Faults
Ma, K., 2021, Aalborg Universitetsforlag. 53 p. (Ph.d.-serien for Det Ingeniør- og Naturvidenskabelige Fakultet, Aalborg Universitet).
Research output: Book/Report › Ph.D. thesis

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Zhu, J., Ma, K., Hajizadeh, A., N. Soltani, M., & Chen, Z. (2017). Fault Detection and Isolation for Wind Turbine Electric Pitch System. In Proceedings of 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS) (pp. 618-623). IEEE Press. IEEE International Conference on Power Electronics and Drive Systems https://doi.org/10.1109/PEDS.2017.8289226

@inproceedings{404562f0a0034bd9afe36e111cf55af2,

title = "Fault Detection and Isolation for Wind Turbine Electric Pitch System",

abstract = "This paper presents a model-based fault detection and isolation scheme applied on electric pitch system of wind turbines. Pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be precisely detected to prevent failures and decrease downtime. To detect faults of electric pitch actuators and sensors, an extended kalman filter (EKF) based multiple model adaptive estimation (MMAE) designed to estimate the states of the system. The proposed method is demonstrated in case studies. The simulation results show that the proposed method detects different fault scenarios of wind turbines under the stochastic external condition.",

author = "Jiangsheng Zhu and Kuichao Ma and Amin Hajizadeh and {N. Soltani}, Mohsen and Zhe Chen",

year = "2017",

month = dec,

doi = "10.1109/PEDS.2017.8289226",

language = "English",

series = "IEEE International Conference on Power Electronics and Drive Systems",

publisher = "IEEE Press",

pages = "618--623",

booktitle = "Proceedings of 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)",

note = "2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS) ; Conference date: 12-12-2017 Through 15-12-2017",

}

Zhu, J, Ma, K, Hajizadeh, A , N. Soltani, M & Chen, Z 2017, Fault Detection and Isolation for Wind Turbine Electric Pitch System. in Proceedings of 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS). IEEE Press, IEEE International Conference on Power Electronics and Drive Systems, pp. 618-623, 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS), Honolulu, United States, 12/12/2017. https://doi.org/10.1109/PEDS.2017.8289226

Fault Detection and Isolation for Wind Turbine Electric Pitch System. / Zhu, Jiangsheng; Ma, Kuichao; Hajizadeh, Amin et al.

Proceedings of 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS). IEEE Press, 2017. p. 618-623 (IEEE International Conference on Power Electronics and Drive Systems).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Fault Detection and Isolation for Wind Turbine Electric Pitch System

AU - Zhu, Jiangsheng

AU - Ma, Kuichao

AU - Hajizadeh, Amin

AU - N. Soltani, Mohsen

AU - Chen, Zhe

PY - 2017/12

Y1 - 2017/12

N2 - This paper presents a model-based fault detection and isolation scheme applied on electric pitch system of wind turbines. Pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be precisely detected to prevent failures and decrease downtime. To detect faults of electric pitch actuators and sensors, an extended kalman filter (EKF) based multiple model adaptive estimation (MMAE) designed to estimate the states of the system. The proposed method is demonstrated in case studies. The simulation results show that the proposed method detects different fault scenarios of wind turbines under the stochastic external condition.

AB - This paper presents a model-based fault detection and isolation scheme applied on electric pitch system of wind turbines. Pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be precisely detected to prevent failures and decrease downtime. To detect faults of electric pitch actuators and sensors, an extended kalman filter (EKF) based multiple model adaptive estimation (MMAE) designed to estimate the states of the system. The proposed method is demonstrated in case studies. The simulation results show that the proposed method detects different fault scenarios of wind turbines under the stochastic external condition.

U2 - 10.1109/PEDS.2017.8289226

DO - 10.1109/PEDS.2017.8289226

M3 - Article in proceeding

T3 - IEEE International Conference on Power Electronics and Drive Systems

SP - 618

EP - 623

BT - Proceedings of 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)

PB - IEEE Press

T2 - 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)

Y2 - 12 December 2017 through 15 December 2017

ER -

Fault Detection and Isolation for Wind Turbine Electric Pitch System

Abstract

Conference

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Research output

Wind Farm Control under Generator Faults

Cite this