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Spectral Subband Centroid Energy Vectors Algorithm and Artificial Neural Networks for Acoustic Emission Pattern ClassificationFLORENTINO, M. T. B. , Da COSTA, E. G. , FERREIRA, T. V. , GERMANO, A. D.
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acoustic emission, artificial neural networks, condition monitoring, corona, insulators
power(10), insulators(9), networks(7), insulation(7), systems(6), partial(6), neural(6), acoustic(6), outdoor(5), speech(4)
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About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 49 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03006
Web of Science Accession Number: 000486574100006
SCOPUS ID: 85072171267
This work proposes and evaluates a methodology for monitoring and diagnosis of polymeric insulators in operation based on the parameterization of acoustic emissions (AE) created by corona and electrical surface discharges. The parameterization was performed with the use of the spectral subband centroid energy vectors (SSCEV) algorithm, which compresses the frequency spectrum and presents the results of the AE energies in several frequency bands. Thus, it was possible to calculate the dominant acoustic emission frequencies. This parameter was used as reference for an operating point of the insulators and, therefore, it was used to classify them. This classification was correlated to the classification obtained by visual inspection in the laboratory, where the insulators were divided into three distinct classes: clean, polluted and damaged. Aiming to insert an aid to the decision-making, this work still proposes the use of artificial neural networks (ANN) for pattern recognition. In this way, we performed a sensitivity analysis of the parameters that influence the SSCEV and ANN, in order to obtain the values and configurations with higher performance. The use of Levenberg-Marquardt training algorithm has proved to be more suitable, since it showed hit rates and convergence up to 97.66 percent and 70 epochs, respectively.
|References|||||Cited By «-- Click to see who has cited this paper|
| Gubanski, S. M. Dernfalk, A., Andersson J., Hillborg, H. "Diagnostic Methods for Outdoor Polymeric Insulators." IEEE Trans. Dielectrics and Electrical Insulation, vol. 14, n. 5, pp. 1065-1080, 2007. |
[CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 118]
 Cigre Working Group B2.21, "Assessment of in-service Composite Insulators by using Diagnostic Tools.", Electra, vol. 269, pp. 29-31, 2013.
 Al-Geelani, N. A., Piah, M. A. M., Bashir, N. "A Review on Hybrid Wavelet Regrouping Particle Swarm Optimization Neural Networks for Characterization of Partial Discharge Acoustic Signals." Renewable and Sustainable Energy Reviews, vol. 45, pp. 20-35, 2015.
 Herrera-Viedma, E., Lopez-Herrera, A. G. "A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling," Int. Journal of Computational Intelligence Systems, vol. 3, n. 4, pp. 420-437, 2010.
[CrossRef] [SCOPUS Times Cited 54]
 Pozna, C., Precup, R., Tar, J. K., Skrjanc, I., Preitl, S. "New results in modelling derived from Bayesian filtering," Knowledge-Based Systems, vol. 23, n. 2, 2010, pp. 182-194.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 53]
 Takacs, A., Kovacs, L., Rudas, I. J., Precup, R., Haidegger, T. "Models for Force Control in Telesurgical Robot Systems," Acta Polytechnica Hungarica, vol. 12, n. 8, 2015, pp. 95-114.
 Ruiz-Rangel, J., Hernandez, C. J. A., Gonzalez, L. M., Molinares, D. J. "ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory," Int. Journal of Artificial Intelligence, vol. 16, n. 1, 2018, pp 214-253.
 Gorur, R. S., Cherney, E. A., Burnham, J. T. Outdoor insulators, 1st ed. Phoenix: Ravi S. Gorur Inc., 1999.
 Vosloo, W. L., Macey, R. E., Tourreil, C. The Practical Guide to High Voltage Insulators. South Africa: Crown Publications cc, vol. 3, pp. 220, 2006.
 Ramirez, C., Moore, P. J. "Identification of surface discharges over new and aged polymeric chain insulators using a non invasive method", In: IEEE Proc. 41st Int. Universities Power Eng. Conf., 2006. pp. 903-906.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]
 Ferreira, T. V., Germano, A. D., Costa, E. G. "Ultrasound and Artificial Intelligence Applied to the Pollution Estimation in Insulations." IEEE Trans. Power Delivery, vol. 12, pp. 583-589, 2012.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12]
 Menon. R., Kolambekar, S., Buch, N. J., Ramamoorty, M. "Correlation of acoustic emission method and electrical method for detection of partial discharges in transformers," in Proc. IEEE 7th Int. Conf. Solid Dielectrics, pp. 299-302, Jun. 2001.
[CrossRef] [Web of Science Times Cited 9]
 Muniraj, C., Chandrasekar, S. "Condition Monitoring of Outdoor Polymeric Insulators Using Wavelets and ANFIS", In: IEEE Int. Conf. on Power and Energy, Kuala Lumpur, 2010, pp. 346-351.
[CrossRef] [SCOPUS Times Cited 1]
 Nyamupangedengu, C., Luhlanga, L. P., Letlape T. "Acoustic and HF Detection of Defects on Porcelain Pin Insulators", In: IEEE Power Eng. Society Conf. and Expo. in Africa, Johannesburg, 2007.
[CrossRef] [SCOPUS Times Cited 9]
 Shurrab, I. Y., El-Hag, A., Assaleh, K., Ghunem, R. "Partial Discharge On-Line Monitoring of Outdoor Insulators", In: IEEE Int. Symp. on Electrical Insulation, San Juan, 2012, pp. 391-394.
[CrossRef] [SCOPUS Times Cited 12]
 Gorur, R. S., Chang, J. W., Amburgey, O. G. "Surface hydrophobicity of polymers used for outdoor insulation", IEEE Trans. Power Delivery, vol. 5, n. 4, pp. 1923-1933, 1990.
[CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 77]
 Huang, C. M., Huang, Y. C. "A novel approach to real-time economic emission power dispatch", IEEE Trans. Power Systems, vol. 18, n. 1, 2003, pp. 288-294,
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 46]
 Kreuger, F. H., Gulski, E., Krivda, A. "Classification of partial discharges", IEEE Trans. Electrical Insulation, vol. 28, n. 6, 1993. pp. 917-931.
[CrossRef] [Web of Science Times Cited 178] [SCOPUS Times Cited 244]
 Ferreira, T. V., Germano, A. D., Silva, K. M., Costa, E. G. "Ultra-sound and Artificial Intelligence Applied to the Diagnosis of Insulations in the Field." High Voltage Engineering, vol. 38, n. 8, pp. 20061-20066, 2012.
 Harrold, R. T. "Acoustic Waveguides for Sensing and Locating Electrical Discharges in High Voltage Power Transformers and other Apparatus." IEEE Trans. Power Apparatus and Systems, vol. 98, n. 2, pp. 449-457, 1979.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 22]
 Lundgaard, L. E. "Partial Discharge XIII: acoustic partial discharge detection-fundamental considerations." IEEE Electrical Insulation Magazine, vol. 8, pp. 25-31, 1992.
[CrossRef] [SCOPUS Times Cited 208]
 Abdel-Salam, M., Abdel-Sattar, S., Sayed, Y., Ghally, M. "Early Detection of Weak Point in MEEC Distribution System." In: Industry Applications Conf. Record of the 2001 IEEE, 2001, Chicago. vol. 4, pp. 2541-2545.
[CrossRef] [SCOPUS Times Cited 5]
 Rocha, P. H. V., Fontgalland, G. "Measuring the radiation bands of overhead power lines glass insulators". Proc. of the IEEE 2014 Int. Conf. Antenna Measurements & Applications. France, 2014.
[CrossRef] [SCOPUS Times Cited 3]
 Dawson, G. A., Richards, C. N., Krider, E. P., Uman, M. A. "The Acoustic Output of a Long Spark". Journal of Geophysical Research, vol. 73, pp. 815-816, 1968.
 Harrold, R. T. "Acoustical Technology Applications in Electrical Insulation and Dielectrics." IEEE Trans. Electrical Insulation, vol. 20, n. 1, pp. 3-19, 1985.
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 41]
 Gajic, B., Paliwal, K. K. "Speech Parametrization for Automatic Speech Recognition in Noisy Conditions," in: Proc. Norwegian Symp. Signal Processing, Trondheim, 2001.
 Paliwal, K. K. "Spectral Subband Centroid Features for Speech Recognition," in: Int. Conf. Acoustics, Speech and Signal Processing, Seattle, vol. 2, pp. 617-620, 1998.
[CrossRef] [SCOPUS Times Cited 112]
 McCulloch, W. S., Pitts, W. "A Logical Calculus of the Ideas Immanent in Nervous Activity." Bulletin of Mathematical Biophysics, vol. 5, pp. 115-133, 1943.
[CrossRef] [SCOPUS Times Cited 7302]
 Haykin, S. O. Neural Networks and Learning Machines. 3. ed. New Jersey: Pearson Prentice Hall, 2008.
 Rosenblatt, F. "The Perceptron: A probabilistic model for information storage and organization in the brain," Psychological Review, vol. 65, pp. 386-408.
[CrossRef] [SCOPUS Times Cited 3692]
 Riedmiller, M., Braun, H. "RPROP - A Fast Adaptive Learning Algorithm", In: Int. Symp. Computer and Information Science, 1993.
 Hagan, M. T, Menhaj, M. B. "Training Feedforward Networks with the Marquardt Algorithm," IEEE Trans. Neural Networks, vol. 5, pp. 989-993, 1994.
[CrossRef] [Web of Science Times Cited 4331] [SCOPUS Times Cited 5314]
 Bishop, C. M. Neural Networks for Pattern Recognition. Clarendon Press, Oxford. 1995.
 Kalman, B. L., Kwasny, S. C. "Why tanh: choosing a sigmoidal function." In: Int. Joint Conf. Neural Networks, 1992, Baltimore. vol. 2, pp. 578 - 581.
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