Intelligent Fault Diagnosis of Wind Turbines Using Adaptive Fuzzy Threshold | ||
| مدل سازی در مهندسی | ||
| Volume 19, Issue 67, December 2021, Pages 151-166 PDF (1.86 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22075/jme.2021.20830.1973 | ||
| Authors | ||
| Nasser Talebi* ; Mohsen Alizadeh | ||
| Assistant Professor, Electrical Engineering Faculty, Yadegare Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran. | ||
| Receive Date: 10 October 2020, Revise Date: 26 May 2021, Accept Date: 29 June 2021 | ||
| Abstract | ||
| Wind turbines are exposed to a variety of faults some of which can cause irreparable economic losses. Therefore, identifying the faults in a short time, ensures the correct operation of the system and prevents the mentioned losses. In this paper, using a dynamic model for wind turbines which includes mechanical and electrical parts with appropriate details, an intelligent fault detection and isolation system is designed utilizing recurrent neural networks. The proposed system can identify the occurred faults in pitch sensors and pitch actuators. Then, in order to consider the robustness of the system, it is suggested to use an adaptive fuzzy threshold in decision making block. Simulation results for the fixed threshold, robust thresholds, and the proposed adaptive fuzzy threshold validate that the suggested adaptive threshold reduces the detection time. In addition, the number of false alarms, and the number of missed ones are reduced by using the intelligent fault detection system. | ||
| Keywords | ||
| Wind Turbine; Dynamic Modelling; Fault Detection and Isolation System; Recurrent Neural Networks; Adaptive Fuzzy Threshold | ||
| References | ||
|
[1] F. D. Bianchi, D. H. Battista, and J. R. Mantz, "Wind Turbine Control Systems", Springer, London, 2007. [2] A. R. Jha, "Wind Turbine Technology", CRC Press-Taylor & Francis Group, Boca Raton, Fla, 2011. [3] N. Talebi, M. A. Sadrnia, and A. Darabi, "Dynamic Response of Wind Energy Conversion Systems Under Various Faults", International Journal of Engineering Systems Modelling and Simulation, Vol. 7, No. 2, 2015, pp. 80-94. [4] محمدجواد عباسی و حمید یعقوبی، "ارائه یک روش ترکیبی جدید جهت شناسایی خطای قطع تحریک و تمایز آن از نوسان توان در ژنراتور القایی دوسو تغذیه"، نشریه مدلسازی در مهندسی، دوره 15، شماره 51، زمستان 1396، صفحه 159- 169. [5] مصطفی سرلک و حسن سعیدی، "مدلی هوشمند و زمان-تطبیقی برای شناسایی خطاهای متقارن و نامتقارن در شرایط نوسان توان"، نشریه مدلسازی در مهندسی، دوره 18، شماره 61، تابستان 1399. [6] C. Sloth, T. Esbensen, and J. Stoustrup, "Robust and Fault-Tolerant Linear Parameter-Varying Control of Wind Turbines", Mechatronics, Vol. 21, No. 4, 2011, pp. 645-659. [7] B. Dolan, "Wind Turbine Modelling, Control and Fault Detection", PhD's Thesis, Technical University of Denmark, 2010. [8] S. M. Tabatabaeipour, P. F. Odgaard, T. Bak, and J. Stoustrup, "Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach", Energies, Vol. 5, 2012, pp. 2424-2448. [9] A. A. Ozdemir, P. Seiler, and G. J. Balas, "Wind Turbine Fault Detection Using Counter-Based Residual Thresholding", Proceedings of the 18th IFAC World Congress, Vol. 44, No. 1, 2011, pp. 8289-8294. [10] S. Donders, "Fault Detection and Identification for Wind Turbine Systems: A Closed-Loop Analysis", Master's Thesis, University of Twente, 2002. [11] A. Kusiak and V. Anoop, "Analyzing Bearing Faults in Wind Turbines: A Data-Mining Approach", Renewable Energy, Vol. 48, 2012, pp. 110-116. [12] H. Badihi, Y. Zhang, and H. Hong, "Wind Turbine Fault Diagnosis and Fault-Tolerant Torque Load Control Against Actuator Faults", IEEE Transactions on Control Systems Technology, Vol. 23, No. 4, 2015, pp. 1351-1372. [13] E. Alizadeh, N. Meskin, and K. Khorasani, "A Negative Selection Immune System Inspired Methodology for Fault Diagnosis of Wind Turbines", IEEE Transactions on Cybernetics, Vol. 47, No. 11, 2017, pp. 3799-3813. [14] M. Entezami, S. Hillmansen, P. Weston, and M.P. Papaelias, "Fault Detection and Diagnosis within a Wind Turbine Mechanical Braking System Using Condition Monitoring", Renewable Energy, Vol. 47, 2012, pp. 175-182. [15] W. Teng, H. Cheng, X. Ding, Y. Liu, Z. Ma, and H. Mu, "DNN-Based Approach for Fault Detection in a Direct Drive Wind Turbine", IET Renewable Power Generation, Vol. 12, No. 10, 2018, pp. 1164-1171. [16] P. Qian, D. Zhang, X. Tian, Y. Si, and L. Li, "A Novel Wind Turbine Condition Monitoring Method Based Oon Cloud Computing", Renewable Energy, Vol. 135, 2019, pp. 390-398. [17] L. Wenyi, Z. Wang, J. Han, and G. Wang, "Wind Turbine Fault Diagnosis Method Based on Diagonal Spectrum and Clustering Binary Tree SVM", Renewable Energy, Vol. 50, 2013, pp. 1-6. [18] I. Valente de Bessa, R. M. Palhares, M. F. S. V. D'Angelo, and J. E. C. Filho, "Data-Driven Fault Detection and Isolation Scheme for a Wind Turbine Benchmark", Renewable Energy, Vol. 87, Part 1, 2016, pp. 634-645. [19] M. Ruiz, L. E. Mujica, S. Alférez, L. Acho, C. Tutivén, Y. Vidal, J. Rodellar, and F. Pozo, "Wind Turbine Fault Detection and Classification by Means of Image Texture Analysis", Mechanical Systems and Signal Processing, Vol. 107, 2018, pp. 149-167. [20] H. S. Dhiman, D. Deb, S. M. Muyeen, and I. Kamwa, "Wind Turbine Gearbox Anomaly Detection based on Adaptive Threshold and Twin Support Vector Machines", IEEE Transactions on Energy Conversion, Vol. 36, No. 4, 2021, pp. 1-8. [21] Y. Cui, P. Bangalore, and L. B. Tjernberg, "A Fault Detection Framework Using Recurrent Neural Networks for Condition Monitoring of Wind Turbines", Wind Energy, Vol. 24, No. 11, 2021, pp. 1-14. [22] N. F. Fadzail, and S. Mat Zali, "Fault Detection and Classification in Wind Turbine by Using Artificial Neural Network", International Journal of Power Electronics and Drive System, Vol. 10, No. 3, 2019, pp. 1687-1693. [23] S. Farsoni, S. Simani, and P. Castaldi, "Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis", Applied Sciences, Vol 11, No. 11, 2021, pp. 1-15. [24] C. Zhang, C. Wen, and J. Liu, "A Deep Neural Network for Wind Turbine Blade Fault Detection", Journal of Renewable and Sustainable Energy, Vol. 12, 2020, pp. 1-9. [25] I. Hwang, S. Kim, Y. Kim, and C. E. Seah, "A Survey of Fault Detection, Isolation, and Reconfiguration Methods", IEEE Transactions on Control Systems Technology, Vol. 18, No. 3, 2010, pp. 636-653. [26] حمید پورباقری، افشین پورتقی و پیام اشتری، "پیشبینی پاسخ دینامیکی سیال در مخازن هوایی آب با استفاده از شبکه عصبی"، نشریه مدلسازی در مهندسی، دوره 15، شماره 48، بهار 1396، صفحه 139- 150. [27] K. Patan, "Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes", Vol. 377, Springer, Berlin, 2008. [28] P. Frasconi, and M. Gori, "Local Feedback Multilayered Networks", Neural Computation, 1992, pp. 120-130. [29] N. Talebi, M. A. Sadrnia, and A. Darabi, "Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks", Computational Intelligence and Neuroscience, Vol. 2014, Article ID 580972, 2014, pp. 1-13. | ||
|
Statistics Article View: 988 PDF Download: 362 |
||