A decision support system based on Machine Learning and Fuzzy Logic Techniques for electrical fault detection and classification
Resumo
A detecção e classificação de falhas em sistemas elétricos são cruciais para garantir fornecimento de energia. Este artigo propõe um sistema de apoio à decisão que combina técnicas de Aprendizado de Máquina e Lógica Fuzzy para detectar, classificar e diagnosticar falhas elétricas. O sistema é composto pelos Módulos de Classificação e de Regras. O primeiro utiliza uma Árvore de Decisão para gerar um conjunto de regras e classificar o estado atual do sistema. O segundo permite ao operador manipular essa base de conhecimento para gerar regras mais inteligíveis e interpretáveis usando Lógica Fuzzy. Os resultados com um conjunto de dados bem conhecido mostram que a proposta supera outros classificadores e pode gerar regras mais simples e interpretáveis.
Palavras-chave:
falhas elétricas, classificação, aprendizado de máquina, lógica fuzzy, explicabilidade
Referências
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Toliyat, H. A., Sadeh, J., and Ghazi, R. (1996). Design of augmented fuzzy logic power system stabilizers to enhance power systems stability. IEEE Transactions on Energy conversion, 11(1):97–103.
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Zhu, Y., Ma, J., Yuan, C., and Zhu, X. (2022). Interpretable learning based dynamic graph convolutional networks for alzheimer’s disease analysis. Information Fusion, 77:53–61.
Cruz, R. M., Sabourin, R., and Cavalcanti, G. D. (2018). Dynamic classifier selection: Recent advances and perspectives. Information Fusion, 41:195–216.
Fahim, S. R., Sarker, Y., Sarker, S. K., Sheikh, M. R. I., and Das, S. K. (2020). Self attention convolutional neural network with time series imaging based feature extraction for transmission line fault detection and classification. Electric Power Systems Research, 187:106437.
Gadzinski, G. and Castello, A. (2022). Combining white box models, black box machines and human interventions for interpretable decision strategies. Judgment and Decision Making, 17(3):598–627.
Goni, M. F., Nahiduzzaman, M., Anower, M., Rahman, M., Islam, M., Ahsan, M., Haider, J., and Shahjalal, M. (2023). Fast and Accurate Fault Detection and Classification in Transmission Lines using Extreme Learning Machine. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 3:100107.
Jamil, M., Sharma, S. K., and Singh, R. (2015). Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus, 4(1):1–13.
Janarthanam, K., Kamalesh, P., Basil, T. V., and Kovilpillai, A. K. J. (2022). Electrical Faults-Detection and Classification using Machine Learning. In 2022 International Conference on Electronics and Renewable Systems (ICEARS), pages 1289–1295.
Lee, Y.-G., Oh, J.-Y., Kim, D., and Kim, G. (2023). Shap value-based feature importance analysis for short-term load forecasting. Journal of Electrical Engineering & Technology, 18(1):579–588.
Liew, X. Y., Hameed, N., and Clos, J. (2021). An investigation of XGBoost-based algorithm for breast cancer classification. Machine Learning with Applications, 6:100154.
Martins, V. E., Cano, A., and Junior, S. B. (2023). Meta-learning for dynamic tuning of active learning on stream classification. Pattern Recognition, 138:109359.
MathWorks (2022). Matlab version: 9.13.0 (r2022b).
Mukherjee, A., Kundu, P. K., and Das, A. (2020). Application of principal component analysis for fault classification in transmission line with ratio-based method and probabilistic neural network: a comparative analysis. Journal of The Institution of Engineers (India): Series B, 101:321–333.
Omar, A. M. S., Osman, M. K., Ibrahim, M. N., Hussain, Z., and Abidin, A. F. (2020). Fault classification on transmission line using LSTM network. Indonesian Journal of Electrical Engineering and Computer Science, 20(1):231–238.
Pan, H., Li, Z., Tian, C., Wang, L., Fu, Y., Qin, X., and Liu, F. (2023). The lightgbm-based classification algorithm for chinese characters speech imagery bci system. Cognitive Neurodynamics, 17(2):373–384.
Sagi, O. and Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. Information Sciences, 572:522–542.
Samantaray, S., Dash, P., and Panda, G. (2007). Distance relaying for transmission line using support vector machine and radial basis function neural network. International Journal of Electrical Power & Energy Systems, 29(7):551–556.
Toliyat, H. A., Sadeh, J., and Ghazi, R. (1996). Design of augmented fuzzy logic power system stabilizers to enhance power systems stability. IEEE Transactions on Energy conversion, 11(1):97–103.
Toraih, E. A., Elshazli, R. M., Hussein, M. H., Elgaml, A., Amin, M., El-Mowafy, M., El-Mesery, M., Ellythy, A., Duchesne, J., Killackey, M. T., et al. (2020). Association of cardiac biomarkers and comorbidities with increased mortality, severity, and cardiac injury in COVID-19 patients: a meta-regression and decision tree analysis. Journal of medical virology, 92(11):2473–2488.
Upendar, J., Gupta, C., and Singh, G. (2008). Discrete wavelet transform and genetic algorithm based fault classification of transmission systems. In 15th National Power Systems Conference, IIT Bombay, pages 323–328.
Wilfling, S., Ebrahimi, M., Alfalouji, Q., Schweiger, G., and Basirat, M. (2022). Learning non-linear white-box predictors: A use case in energy systems. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), pages 507–512.
Zhu, X., Li, J., Ren, J., Wang, J., and Wang, G. (2023). Dynamic ensemble learning for multi-label classification. Information Sciences, 623:94–111.
Zhu, Y., Ma, J., Yuan, C., and Zhu, X. (2022). Interpretable learning based dynamic graph convolutional networks for alzheimer’s disease analysis. Information Fusion, 77:53–61.
Publicado
25/09/2023
Como Citar
CARVALHO, Davi; MELO, José; SILVA, Eraylson G.; MATTOS NETO, Paulo S. G. de.
A decision support system based on Machine Learning and Fuzzy Logic Techniques for electrical fault detection and classification. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 418-431.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234233.
