Abstract
This paper presents a method for automatic classification of faults and transients in power distribution networks, based on voltage oscillographies of the distribution networks feeders. For signal preprocessing, the discrete wavelet transform was used with the performances of several families of wavelet functions being compared. In the classification stage, three neural models were assessed: multi-layer perceptrons, radial basis function networks, and support vector machines. The models were trained autonomously, i.e., using automatic model selection and complexity control. Promising results were obtained using a set of simulations generated using the Alternative Transients Program (ATP). Initial results obtained for real data acquired from a set of oscillograph loggers installed in a distribution network are also presented.
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References
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.
Cataltepe, Z., Abu-Mostafa, Y. S., & Magdon-Ismail, M. (1999). No free lunch for early stopping. Neural Computation, 11, 995–1009.
Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46(1–3), 131–159.
Cherkassky, V., & Mulier, F. F. (1998). Learning from data: Concepts, theory and methods. New York: Wiley.
Costa, F. B., Brito, N. S. D., & Souza, B. A. (2010). Detecção de faltas evolutivas e múltiplos distúrbios em registros oscilográficos baseada na transformada wavelet discreta. Controle e Automação, 21, 173–184. In Portuguese.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.
Demir, Y. (2010). Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines. Electric Power Systems Research, 80, 743–752.
Dong, X., Kong, W., & Cui, T. (2009). Fault classification and faulted-phase selection based on the initial current traveling wave. IEEE Transactions on Power Delivery, 24, 552–559.
Ferreira, V. H., & da Silva, A. P. A. (2007). Toward estimating autonomous neural network load forecasters. IEEE Transactions on Power Systems, 22(4), 1554–1562.
Konaka, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety, 15, 992–1007.
Lazzaretti, A. E., Ferreira, V. H., Vieira Neto, H., Riella, R. J., & Omori, J. (2009). Classification of events in distribution networks using autonomous neural models. In Proceedings of the 15th international conference on intelligent system applications to power systems. Curitiba, Brazil.
Lazzaretti, A. E., Ravaglio, M. A., Toledo, L. F. R. B., Teixeira-Júnior, J. A., Rojas, P. M., & Pinto, C. L. S. (2011). Measurements of lightning discharges in overhead distribution feeders. In Anais do XI Simpósio Internacional de Proteção Contra Descargas Atmosféricas. In Portuguese.
Mahanty, R. N., & Gupta, P. B. D. (2007). A fuzzy logic based fault classification approach using current samples only. Electric Power Systems Research, 77, 501–507.
Malathi, V., Marimuthu, N. S., & Baskar, S. (2010). Intelligent approaches using support vector machine and extreme learning machine for transmission line protection. Neurocomputing, 73, 2160–2167.
Mallat, S. (1999). A wavelet tour of signal processing (2nd ed.). San Diego, CA: Academic Press.
Manimala, K., Selvi, K., & Ahila, R. (2011). Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining. Applied Soft Computing, 11, 5485–5497.
Marsland, S., Shapiro, J., & Nehmzow, U. (2002). A self-organising network that grows when required. Neural Networks, 15, 1041–1058.
Oleskovicz, M., Aggarwal, R. K., & Coury, D. V. (2003). O emprego de redes neurais artificiais na detecção, classificação e localização de faltas em linhas de transmissão. Controle e Automação, 14, 138–150. In Portuguese.
Riella, R. J., Ferrari, V. P., Paulillo, G., Ortega, M. R., & Pereira, J. G. (2008). Desenvolvimento de um sistema de monitoramento contínuo da qualidade da energia elétrica para subestações de distribuição. In Anais do XVIII Seminário Nacional de Distribuição de Energia Elétrica. In Portuguese.
Vapnik, V., & Chapelle, O. (2000). Bounds on error expectation for support vector machines. Neural Computation, 12(9), 2013–2036.
Zhang, N., & Kezunovic, M. (2007). A real time fault analysis tool for monitoring operation of transmission line protective relay. Electric Power Systems Research, 77, 361–370.
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The authors would like to thank Copel and ANEEL for their funding of this research, and Cleverson L. S. Pinto for his contribution in this work.
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Lazzaretti, A.E., Ferreira, V.H., Neto, H.V. et al. Autonomous Neural Models for the Classification of Events in Power Distribution Networks. J Control Autom Electr Syst 24, 612–622 (2013). https://doi.org/10.1007/s40313-013-0064-8
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DOI: https://doi.org/10.1007/s40313-013-0064-8