Rotor Poles Abnormality Detection of Wind Generator Based on ACO Combined with BPNN

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


This paper proposes an ABPNN approach, which utilizes ant colony optimization (ACO) to obtain optimal weight of back propagation network (BPNN). And various abnormalities of wind generator rotor can be accurately recognized. In the paper, four tailor-made types of wind generators, namely one broken rotor pole, two adjacent broken rotor poles, fractured rotor pole and burnt rotor pole, are operated to obtain the measured current signals of generators under various load and speed conditions. The spectrums and resolution levels of the current signals can be illustrated by using S-transform (ST) and multi-resolution analysis (MRA) respectively, and features can be extracted from the spectrums and resolution levels. Using the proposed ABPNN approach, the significant optimal weight of BPNN can be calculated based on the extracted features. Finally, the results show that the recognition accuracy of various abnormal generator rotor poles can be improved, even in environments with noise interference
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Ant Colony Optimization; Back Propagation Network; S-Transform

Full Text:



Abdullah Asuhaimi Mohd Zin, Maiza Ismail, Sazali P Abdul Karim, Arcing Fault Detection for a Single Busbar Metal Enclosed Switchgear, (2009) International Review on Modelling and Simulations (IREMOS), 2 (2), pp. 191-195.

Arkan, M., Çaliş, H., Taǧluk, M.E., Bearing and misalignment fault detection in induction motors by using the space vector angular fluctuation signal, (2005) Electrical Engineering, 87 (4), pp. 197-206.

M. A. Ríos, J. L. Sánchez, C. J. Zapata, O. Gómez, Improved Branch Participation Factor in Voltage Stability Assessment, (2009) International Review on Modelling and Simulations (IREMOS), 2 (1), pp. 18-24.

Akar, M., Detection of rotor bar faults in field oriented controlled induction motors, (2012) Journal of Power Electronics, 12 (6), pp. 982-991.

H. Douglas, P. Pillay, A.K. Ziarani, Broken Rotor Bar Detection in Induction Machines With Transient Operating Speeds, IEEE Transactions on Energy Conversion, vol. 20, 2006, pp. 135-141.

F. Immovilli, A. Bellini, R. Rubini and C. Tassoni, Diagnosis of bearing faults in induction machines by vibration or current signals:A critical comparison, IEEE Transactions on Industry Applications, vol. 46, n. 4, 2010 , pp. 1350-1359.

Missing bars of rotors Motion system design, [On-lineavailable]

Broken bars of rotors, The MCSA interpreter, [On-line available]

W.L. Cameron, Precise expression relating the Fourier transform of a continuous signal to the fast Fourier transform of signal samples, IEEE Transactions on Signal Processing Society, vol. 43, n. 12, Dec. 1995, pp. 2811-2821.

I.S. Reed, and T.K. Truong, A new hybrid algorithm for computing a Fast Discrete Fourier transform, IEEE Transactions on Computer Society, vol. 28, n. 7, July. 1979, pp. 487-492.

Z.L. Gaing, Wavelet-based neural network for power disturbance recognition and classification, IEEE Transactions on Power & Energy Society, vol. 19, n. 4, Oct. 2004, pp. 1560-1568.

T.J. Burns, S.K. Rogers, M.E. Oxley, and D.W. Ruck, A wavelet multiresolution analysis for spatio-temporal signals, IEEE Transactions on Aerospace & Electronic Systems Society, vol. 32, n. 2, , April. 1996 pp. 628-649.

Karci, H., Tohumoglu, G., Linear time-varying systems analysis in wavelet domain, (2007) Electrical Engineering, 89 (8), pp. 653-658.

Elango, M.K., Nirmal Kumar, A., Purushothaman, S., Application of Hilbert Huang transform with locally weighted projection regression method for power quality problems, (2010) International Review of Electrical Engineering (IREE), 5 (5), pp. 2405-2412.

M. Schimmel, and J. Gallart, The inverse S-transform in filters with time-frequency localization, IEEE Transactions on Signal Processing, vol. 53, n. 11, 2005 , pp. 4417-4422.

F. Zhao, and R. Yang, Power-quality disturbance recognition using S-transform, IEEE Transactions on Power & Energy Society, vol. 22, n. 2, April. 2007, pp. 944-950.

M. Dorigo, M. Birattari, and T. Stutzle, Ant colony optimization, IEEE Transactions on Computional Intelligence, vol. 1, n. 4, 2006, pp. 28-39.

Chiha, I., Liouane, H., Liouane, N., A hybrid method based on Multi-Objective Ant Colony optimization and differential evolution to design PID DC motor speed controller, (2012) International Review on Modelling and Simulations (IREMOS), 5 (2), pp. 905-912.

Massim, Y., Meziane, R., Zeblah, A., Rahli, M., Ant colony optimization for multi-state series-parallel system expansion scheduling, (2005) Electrical Engineering, 87 (6), pp. 327-336.

Y.C. Liang, and A.E. Smith, Ant colony optimization algorithm for the redundancy allocation problem, IEEE Transactions on Reliability, vol. 53, n. 3, 2004, pp. 417-423.

R.C. Lacher, S.I. Hruska, and D.C Kuncicky, Back-propagation learning in expert networks,”IEEE Transactions on Neural Networks, vol. 3, n. 1, 1992, pp. 62-72.

E.D. Karnin, A simple procedure for pruning back-propagation trained neural networks, IEEE Transactions on Neural Networks, vol. 1, n. 2, 1990, pp. 239-242.

S. Tamura, and M. Tateishi, Capabilities of a four-layered feedforward neural network: four layers versus three, IEEE Transactions on Neural Networks, vol. 8, n. 2, 1997, pp. 251-255.

G.B. Huang, L. Chen, and C.K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks, vol. 17, n. 4, 2006, pp. 879-892.

Damiano, A., Gatto, G., Marongiu, I., Meo, S., Perfetto, A., Serpi, A., A direct-drive wind turbine control for a wind power plant with an internal DC distribution system, (2012) International Review of Electrical Engineering (IREE), 7 (4), pp. 4845-4856.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize