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Research Article

Proactive maintenance of small wind turbines using IoT and machine learning models

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Pages 463-475 | Received 30 Dec 2020, Accepted 11 Apr 2021, Published online: 13 Jun 2021

References

  • Andrew Swartz, R., P. Lynch Jerome, Z.Stephan, S.Bert, Raimund Rolfes. 2010. Structural Monitoring of Wind Turbines using Wireless Sensor Networks, Smart Structures and Systems, Energy: Vol. 6, Issue No: 3:183–196.
  • Adrian Stetco, Fateme Dinmohammadi, Xingyu Zhao, Valentin Robu, David Flynn, Mike Barnes, John Keane, Goran Nenadic, Machine learning methods for wind turbine condition monitoring: A Review https://doi.org/https://doi.org/10.1016/j.renene.2018.10.047
  • Besnard, F., L.Bertling, 2010. An Approach for Condition-Based Maintenance Optimization applied to Wind Turbine Blades, IEEE Trans 1:77–83.
  • Dhiman, Harsh & Deb, Dipankar & Anand, Pritam. (2018). Wavelet Transform and Variants of SVR with Application in Wind Forecasting. https://doi.org/10.1007/978-981-13-1966-2_45.
  • Eduardo, J., B.Alvareza, P.Adrijan Ribarica. 2017. An Improved-Accuracy method for Fatigue Load Analysis of Wind Turbine Gearbox based on SCADA, Elsevier 115:391–399
  • Fischer, K., F.Besnard, L.Bertling, 2012. Reliability-Centered Maintenance for Wind Turbines based on Statistical Analysis and Practical Experience, IEEE Trans. Energy Convers 27:184–195.
  • Faleh H. Mahmood, Ali K. Resen, Ahmed B. Khamees, Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq, Energy Reports, Volume 6, Supplement 3, 2020, Pages79–87, https://doi.org/https://doi.org/10.1016/j.egyr.2019.10.021.
  • Harsh S. Dhiman, Dipankar Deb, Josep M. Guerrero, Hybrid machine intelligent SVR variants for wind forecasting and ramp events, Renewable and Sustainable Energy Reviews, Volume 108, 2019, Pages369–379, ISSN1364-0321 .
  • H. S. Dhiman, D. Deb, J. Carroll, V. Muresan, and M.-L. Unguresan, Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis, Sensors, vol. 20, no. 23, p. 6742, Nov. 2020.
  • Hugh Boyes, Bil Hallaq, Joe Cunningham, Tim Watson, The industrial internet of things (IIoT): An analysis framework, Computers in Industry, Vol. 101, 2018, Pages1–12, https://doi.org/https://doi.org/10.1016/j.compind.2018.04.015.
  • Jannis Tautz, W., J. Watson Simon. 2016. Using SCADA Data for Wind Turbine Condition Monitoring – A Review IET Renewable Power Generation doi: https://doi.org/10.1049/iet-rpg.2016.0248.
  • Pandit, Ravi Kumar, et al., Comparison of advanced non-parametric models for wind turbine power curves, IET Renewable Power Generation (2019), 13 (9):1503
  • Yanting Li, Shujun Liu, Lianjie Shu, Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data, Renewable Energy, Volume 134 2019, Pages357–366, ISSN0960-1481 .
  • Ravi Kumar Pandit, David Infield, Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model, International Journal of Prognostics and Health Management, ISSN 2153-2648, 2018 023,2018
  • R. K. Pandit and D. Infield, “SCADA-based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes“ in IET Renewable Power Generation, vol. 12, no. 11, pp. 1249–1255, 208 2018, DOI: https://doi.org/10.1049/it-rpg.2018.0156
  • Phong Dao, B., J.Wieslaw Staszewski, B.Tomasz, TadeuszUhl. 2017. Condition Monitoring and Fault Detection in Wind Turbines based on Cointegration Analysis of SCADA Data, Elsevier Renewable Energy 116: 107–122.
  • Zhao, Yingying & Li, Dongsheng & Dong, Ao & Kang, Dahai &Lv, Qin & Shang, li. (2017). Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data. Energies. 10. 1210. https://doi.org/10.3390/en10081210.
  • Patel, Pranav & Shandilya, Ashish & Deb, Dipankar. (2017). Optimized Hybrid Wind Power Generation with Forecasting Algorithms and Battery Life Considerations. https://doi.org/10.1109/PECI.2017.7935735.
  • Mengnan Cao & Yingning Qiu & Yanhui Feng & Hao Wang & Dan Li, 2016. “Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data,” Energies, MDPI, Open Access Journal, vol. 9(10), pages 1–18.
  • Pierre, T., W.René, Mohand Ouhrouche, H.Fouad Slaoui, T.Tommy Andy and E.Gabriel. 2014. Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges, Energies 7, 2595–2630; doi:https://doi.org/10.3390/en7042595.
  • Tchakoua, P., R.Wamkeue, T.A Tameghe, G. Ekemb.2013. Review of Concepts and for Wind Turbines Condition Monitoring, In Proceedings of the 2013 World Congress on Computer and Information Technology (WCCIT), Tunisia: 22–24:1–9.
  • Tianyang Wang, Qinkai Han, Fulei Chu, Zhipeng Feng, Vibration-based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review, Mechanical Systems, and Signal Processing, Volume 126 2019, Pages 662–685, ISSN 0888-3270.
  • P. Guo and D. Infield, “Wind Turbine Power Curve Modeling and Monitoring with Gaussian Process and SPRT,” in IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 107–115, Jan. 2018, doi: https://doi.org/10.1109/TSTE.2018.2884699.
  • D. Kapoor, D. Deb, A. Sahai and H. Bangar, “Adaptive failure compensation for coaxial rotor helicopter under propeller failure,” 2012 American Control Conference (ACC), Montreal, QC, 2012, pp. 2539–2544, doi: https://doi.org/10.1109/ACC.2012.6315636.
  • Ribrant, J., 2006, Reliability Performance and Maintenance—A Survey of Failures in Wind Power Systems Thesis, KTH, Master’s Royal Institute of Technology.
  • Saad,C., MostafaBaghouri, 2014.AbderrahmaneHajraoui. Real Time Remote Monitoring and Fault Detection in Wind Turbine, International Scholarly and Scientific Research & Innovation 8:(9).
  • L. Xiao, N. B. Mandayam and H. Vincent Poor, “Prospect Theoretic Analysis of Energy Exchange Among Microgrids,” in IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 63–72, Jan. 2015, doi: https://doi.org/10.1109/TSG.2014.2352335.
  • MeikSchlechtingena, S.Ilmar Ferreira. 2013. Wind Turbine Condition Monitoring based on SCADA Data using normal behavior models, ELSEVIER Vol 14:447–460.
  • Naidu K, Ali MS, Abu Bakar AH, Tan CK, Arof H, Mokhlis H (2020) Optimized artificial neural network to improve the accuracy of estimated fault impedances and distances for underground distribution system. PLoS ONE 15(1): e0227494. https://doi.org/https://doi.org/10.1371/journal.pone.0227494.
  • R Patel, A Gojiya, D Deb, Failure reconfiguration of pumps in two reservoirs connected to overhead tank Innovations in Infrastructure, Innovations in Infrastructure, 2019, Volume 757, ISBN: 978-981-13-1965-5, 81–92.
  • Yoganand.S, chithra.s, 2017 “Condition monitoring of small wind turbines using IoT (A statistical survey and Analysis)”, International Journal of Computer & Mathematical Sciences, vol 6, Issue:7, PP. 140–147, Academic Science.
  • Yoganand.S, Ms.Nithya.A, Dr. S. Chithra. 2019 “A Low Cost Pollution Free System (PFS) for Smart Cities using Internet of Things.” Journal of emerging technologies and innovative research, vol 6, Issue:4, pp. 266–270, April .
  • R. Yugha and S. Chithra, “A survey on technologies and security protocols: Reference for future generation IoT”, Journal of Network and Computer Applications, Accepted 1 July 2020 Available online 12 August 2020, Elsevier publications.
  • Kabilan K., Bhalaji N.,Chithra S.” Analysis of 6LOWPAN and CoAP Protocols for Maternal Health Care”, Lecture Notes in Electrical Engineering, vol 521, pp.171-180, Springer, Singapore, 2019.
  • Sundhara Kumar K.B, Krishna G, Bhalaji N, Chithra S, “BCI cinematics –A pre-release analyser for movies using H 2 O deep learning platform”, Computers and Electrical Engineering, Volume 74, Pages547–556, March 2019.
  • S. Thanga Revathi, N. Ramaraj and S. Chithra, “Brain storm-based Whale Optimization Algorithm for privacy protected data publishing in cloud computing”, Cluster Computing, Feb 2018, Pg. 1-10 Springer, https://doi.org/http://doi.org/10.1007/s10586-018-2200-5
  • N. Bhalaji, K.B. Sundhara Kumar and Chithra Selvaraj, “Empirical study of feature selection methods over classification algorithms”, International Journal of Intelligent Systems Technologies and Applications, Vol. 17, Nos. 1/2 2018, pg 98–108, Inderscience publication.
  • Nazir MS, Alturise F, Alshmrany S, Nazir HMJ, Bilal M, Abdalla AN, Sanjeevikumar P, M. Ali Z. Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend. Sustainability. 2020; 12(9):3778 https://doi.org/https://doi.org/10.3390/su12093778.
  • Nguyen DPN, Lauwaert J. Calculating the Energy Yield of Si-Based Solar Cells for Belgium and Vietnam Regions at Arbitrary Tilt and Orientation under Actual Weather Conditions. Energies. 2020; 13(12):3180. https://doi.org/https://doi.org/10.3390/en13123180.
  • Wind Turbine Power Calculations”, RWE npower renewables Mechanical and Electrical Engineering Power Industry Royal Academy of Engineering, 2010.
  • Yingning Qiu, Yanhui Feng, David Infield, 2020 Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method, Renewable Energy, Volume 145, Pages 1923–1931, ISSN 0960-1481.
  • https://openei.org/datasets/
  • https://www.nrel.gov/wind/data-tools.html

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