Early fault prediction of a wind turbine using a novel ANN training algorithm based on ant colony optimization

Year 2019, Volume: 3 Issue: 4, 139 - 147, 31.12.2019

Yunus Eroğlu , Serap Ulusam Seçkiner

https://doi.org/10.30521/jes.613315

Cited By: 10

Abstract

The technological
developments in wind energy field have reduced the investment and the operation
costs. For this reason, wind farms have become more popular around the world.
Increasing the share of wind energy in the market has led to the need for
secure, inexpensive, and effective monitoring and control approaches. In the
present work, various monitoring and control tools, which are cheap and easy to
implement in wind farms using existing system data are proposed. The primary
purpose of this study is to offer a new methodology, i.e. an artificial neural
network (ANN) design with a novel training algorithm called Antrain ANN, in
order to explore the early fault detection in a wind turbine. Our case problem
is the fault detection for a wind turbine. For this issue, we used real data
consisting of 873 samples with 12 inputs and one output. The models used in the
work try to forecast fault occurrence before 10 minutes it happens.  The proposed Antrain ANN algorithm is
compared with Quick Propagation, Conjugate Gradient Descent, Quasi-Newton,
Limited Memory Quasi-Newton, Online Backpropagation, and Batch Back Propagation
algorithms, respectively. The results have shown that the proposed novel
approach has better results in the correct classification rates than other
algorithms except the Quasi-Newton and Limited Memory Quasi-Newton ones.

Keywords

Ant colony algorithm, Artificial neural networks, Fault detection, Wind energy, Wind turbine

References

• Tautz-Weinert, J., Watson, S. J. Using SCADA data for wind turbine condition monitoring – a review, IET Renewable Power Generation 2016, 11(4), 382-394.
• Yang, W., Tavner, P. J., Crabtree, C. J., Feng, Y., Qiu, Y. Wind turbine condition monitoring: technical and commercial challenges, Wind Energy 2014, 17(5), 673–693.
• Kandukuri, S. T., Klausen, A., Karimi, H. R., Robbersmyr, K. G. A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management, Renewable and Sustainable Energy Reviews 2016, 53, 697–708.
• Zaher, A. S., McArthur, S. D. J. A Multi-Agent Fault Detection System for Wind Turbine Defect Recognition and Diagnosis. In Power Tech, 2007 IEEE Lausanne (pp. 22–27). Presented at the Power Tech, 2007 IEEE Lausanne. doi:10.1109/PCT.2007.4538286
• Qiu, Y., Feng, Y., Sun, J., Zhang, W., Infield, D. Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data. IET Renewable Power Generation 2016, 10(5), 661–668.
• Wang, L., Zhang, Z., Xu, J., Liu, R. Wind Turbine Blade Breakage Monitoring with Deep Autoencoders, IEEE Transactions on Smart Grid 2016, 1(1), 99.
• Liu, T. Fault diagnosis of gearbox by selective ensemble learning based on artificial immune algorithm. In 2016 3rd International Conference on Systems and Informatics (ICSAI) 2016, 460–464. Presented at the 2016 3rd International Conference on Systems and Informatics (ICSAI). doi:10.1109/ICSAI.2016.7810999
• Yang, W., Tavner, P. J., Crabtree, C. An Intelligent Approach to the Condition Monitoring of Large Scale Wind Turbines. In European Wind Energy Conference 2009. Marseille, France.
• Yang, W., Tian, S. W. Research on a power quality monitoring technique for individual wind turbines. Renewable Energy 2015, 75, 187–198. doi:10.1016/j.renene.2014.09.037
• Lu, B., Li, Y., Wu, X., Yang, Z. A review of recent advances in wind turbine condition monitoring and fault diagnosis. In IEEE Power Electronics and Machines in Wind Applications 2009. PEMWA 2009 (pp. 1–7). Presented at the IEEE Power Electronics and Machines in Wind Applications, 2009. doi:10.1109/PEMWA.2009.5208325
• Schlechtingen, M., Santos, I. F., Achiche, S. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing 2013. 13(1) 259–270. doi:10.1016/j.asoc.2012.08.033
• Zhang, Z., Verma, A., Kusiak, A. Fault Analysis and Condition Monitoring of the Wind Turbine Gearbox. IEEE Transactions on Energy Conversion 2012. 27(2) 526–535. doi:10.1109/TEC.2012.2189887
• Zhang, Z., Kusiak, A. Monitoring Wind Turbine Vibration Based on SCADA Data. Journal of Solar Energy Engineering 2012, 134(2) 021004–021004. doi:10.1115/1.4005753
• Kusiak, A., Verma, A. A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines. IEEE Transactions on Sustainable Energy 2011. 2(1) 87–96. doi:10.1109/TSTE.2010.2066585
• Kusiak, A., Zheng, H., Song, Z. Models for monitoring wind farm power. Renewable Energy 2009. 34:3: 583–590. doi:10.1016/j.renene.2008.05.032
• Seçkiner, SU, Eroğlu, Y, Emrullah, M. Dereli T. Ant colony optimization for continuous functions by using novel pheromone updating. Applied mathematich and Computation 2013. 219(9), 4163-4175.
• McCulloch W. S. Pitts, W. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 1943. 5(4), 115-133.
• Yadav, RN, Kumar, N, Kalra, PK, John, J “Learning with generalized-mean neuron model”, Neurocomputing. 2006. 69(16-18), 2026-203.
• Ghosh-Dastidar S. Adeli, H. A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Networks 2009. 22(10) 1419-1431.
• Shiblee, M. Chandra, B. ve Kalra, P. K. Learning of geometric mean neuron model using resilient propagation algorithm. Expert Syst. Appl. 2010. 37(12), 7449-7455.
• Chau, K. W. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J. Hydrol. 2006. 329(3-4), 363-367.
• Wang, G. Hao, J. Ma J., ve Huang, L. A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering, Expert Syst. Appl. 2010. 37(9), 6225-6232.
• Bas E., Uslu V. R., Egrioglu E. Robust learning algorithm for multiplicative neuron model artificial neural networks. Expert Syst. Appl. 2016. 56, 80-88.
• Mohamad E. T., Faradonbeh R. S., Armaghani D. J., Monjezi M., Majid M. Z. A. An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput. Appl. 2016. 1-14.
• Lee A., Geem Z. W., Suh K. D. Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones. Appl. Sci. 2016. 6(6), 164.
• Ganguly S., Patra A., Chattopadhyay P. P., Datta S. New training strategies for neural networks with application to quaternary Al–Mg–Sc–Cr alloy design problems. Appl. Soft Comput. 2016. 46, 260-266.
• Bas E. The Training of Multiplicative Neuron Model Based Artificial Neural Networks With Differential Evolution Algorithm For Forecasting. J. Artif. Intell. Soft Comput. Res. 2016. 6(1), 5-11.
• Li J.B. and Chung Y.K. A Novel Back-propagation Neural Network Training Algorithm Designed by an Ant Colony Optimization. 2005 IEEE/PES Transmission Distribution Conference Exposition: Asia and Pacific 2005. 1-5.
• Socha K. and Blum C. An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput. Appl. 2007. 16(3), 235-247.
• Saghatforoush A., Monjezi M., Faradonbeh R. S., Armaghani D. J. Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Eng. Comput. 2016. 32(2), 255-266.