O Uso de AutoEncoders como Fomentador de Medidas em Sistemas de Distribuição
Resumo
O sucesso de funções de automação e controle vislumbradas para as redes de distribuição inteligentes depende da supervisão confiável da rede em tempo real. Esta tarefa é realizada pelo estimador de estado da distribuição, responsável por processar um conjunto de medidas recebidas através do sistema de aquisição de dados. Em redes inteligentes, a infraestrutura avançada de medição (AMI) permite a leitura regular de medidas de tensão e potência dos consumidores, as quais podem complementar as poucas medidas usualmente presentes na rede de distribuição (medidas SCADA) e beneficiar o processo de estimação de estado. Entretanto, devido a limitações na infraestrutura de comunicação, medidas SCADA e AMI possuem taxas de amostragem e aquisição diferentes. Este trabalho apresenta uma metodologia que emprega uma rede neural do tipo AutoEncoder para a geração de pseudomedidas AMI para complementar medidas SCADA em instantes de tempo em que apenas estas últimas estão disponíveis para processamento. Simulações realizadas com um sistema de distribuição de 34 barras ilustram a metodologia proposta e os resultados obtidos confirmam o seu potencial de aplicação para a geração de pseudomedidas.
Palavras-chave:
Redes inteligentes, Estimação de estado, Codificadores Automáticos, Redundância, Supervisão em tempo real
Referências
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Dehghanpour, K., Yuan, Y., Wang, Z., and Bu, F. (2019). A game-theoretic data-driven approach for pseudo-measurement generation in distribution system state estimation. IEEE Transactions on Smart Grid, 10(6):5942–5951.
He, Z., Hong, K., Zhou, J., Liang, D., Wang, Y., and Liu, Q. (2022). Deep frequencyrecurrent priors for inverse imaging reconstruction. Signal Processing, 190:108320.
Hinton, G. E. and Zemel, R. (1994). Autoencoders, minimum description length and helmholtz free energy. In Cowan, J., Tesauro, G., and Alspector, J., editors, Advances in Neural Information Processing Systems, volume 6. Morgan-Kaufmann.
Huang, S.-C., Lu, C.-N., and Lo, Y.-L. (2015). Evaluation of ami and scada data synergy for distribution feeder modeling. IEEE Transactions on Smart Grid, 6(4):1639–1647.
Jabbar, A., Li, X., and Omar, B. (2020). A survey on generative adversarial networks: Variants, applications, and training.
Ko, J. U., Na, K., Oh, J.-S., Kim, J., and Youn, B. D. (2021). A new auto-encoder-based dynamic threshold to reduce false alarm rates for anomaly detection of steam turbines. Expert Systems with Applications, page 116094.
Krstulovic, J., Miranda, V., Costa, A. J. S., and Pereira, J. (2013). Towards an autoassociative topology state estimator. IEEE transactions on power systems, 28(3):3311–3318.
Lefebvre, S., Prévost, J., and Lenoir, L. (2014). Distribution state estimation: A necessary requirement for the smart grid. In 2014 IEEE PES General Meeting — Conference Exposition, pages 1–5.
Li, H., Lai, L., and Zhang, W. (2011). Communication requirement for reliable and secure state estimation and control in smart grid. IEEE Transactions on Smart Grid, 2(3):476–486.
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., and Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234:11–26.
Mestav, K. R., Luengo-Rozas, J., and Tong, L. (2019). Bayesian state estimation for IEEE Transactions on Power unobservable distribution systems via deep learning. Systems, 34(6):4910–4920.
Miranda, V., Krstulovic, J., Keko, H., Moreira, C., and Pereira, J. (2011). Reconstructing missing data in state estimation with autoencoders. IEEE Transactions on power systems, 27(2):604–611.
Singh, R., Pal, B., and Jabr, R. (2009). Choice of estimator for distribution system state estimation. IET generation, transmission & distribution, 3(7):666–678.
Soares, W., de Souza, J. C. S., Do Coutto Filho, M. B., and Augusto, A. A. (2019). Distribution system state estimation with real-time pseudo-measurements. In 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), pages 1–5. IEEE.
Vijayalakshmi, G. and Shanthakumar, M. (2019). Image restoration on fusion of mammographs and mri breast images using dual tree complex wavelet transform. International Journal of Advanced Science and Technology, 28(17):842–857. cited By 0.
Xu, W., Zhang, J., Li, X., Yuan, S., Ma, G., Xue, Z., Jing, X., and Cao, J. (2022). Intelligent denoise laser ultrasonic imaging for inspection of selective laser melting components with rough surface. NDT & E International, 125:102548.
Yilmaz, C. S., Yilmaz, V., and Gungor, O. (2022). A theoretical and practical survey of image fusion methods for multispectral pansharpening. Information Fusion, 79:1–43.
Zhang, X., Mu, J., Zhang, X., Liu, H., Zong, L., and Li, Y. (2022). Deep anomaly detection with self-supervised learning and adversarial training. Pattern Recognition, 121:108234.
Zhao, J., Huang, C., Mili, L., Zhang, Y., and Min, L. (2020). Robust medium-voltage distribution system state estimation using multi-source data. In 2020 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1–5.
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011). Algorithms for hyperparameter optimization. In Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems, volume 24. Curran Associates, Inc.
Dahale, S., Karimi, H. S., Lai, K., and Natarajan, B. (2020). Sparsity based approaches for distribution grid state estimation-a comparative study. IEEE Access, 8:198317–198327.
Dehghanpour, K., Yuan, Y., Wang, Z., and Bu, F. (2019). A game-theoretic data-driven approach for pseudo-measurement generation in distribution system state estimation. IEEE Transactions on Smart Grid, 10(6):5942–5951.
He, Z., Hong, K., Zhou, J., Liang, D., Wang, Y., and Liu, Q. (2022). Deep frequencyrecurrent priors for inverse imaging reconstruction. Signal Processing, 190:108320.
Hinton, G. E. and Zemel, R. (1994). Autoencoders, minimum description length and helmholtz free energy. In Cowan, J., Tesauro, G., and Alspector, J., editors, Advances in Neural Information Processing Systems, volume 6. Morgan-Kaufmann.
Huang, S.-C., Lu, C.-N., and Lo, Y.-L. (2015). Evaluation of ami and scada data synergy for distribution feeder modeling. IEEE Transactions on Smart Grid, 6(4):1639–1647.
Jabbar, A., Li, X., and Omar, B. (2020). A survey on generative adversarial networks: Variants, applications, and training.
Ko, J. U., Na, K., Oh, J.-S., Kim, J., and Youn, B. D. (2021). A new auto-encoder-based dynamic threshold to reduce false alarm rates for anomaly detection of steam turbines. Expert Systems with Applications, page 116094.
Krstulovic, J., Miranda, V., Costa, A. J. S., and Pereira, J. (2013). Towards an autoassociative topology state estimator. IEEE transactions on power systems, 28(3):3311–3318.
Lefebvre, S., Prévost, J., and Lenoir, L. (2014). Distribution state estimation: A necessary requirement for the smart grid. In 2014 IEEE PES General Meeting — Conference Exposition, pages 1–5.
Li, H., Lai, L., and Zhang, W. (2011). Communication requirement for reliable and secure state estimation and control in smart grid. IEEE Transactions on Smart Grid, 2(3):476–486.
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., and Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234:11–26.
Mestav, K. R., Luengo-Rozas, J., and Tong, L. (2019). Bayesian state estimation for IEEE Transactions on Power unobservable distribution systems via deep learning. Systems, 34(6):4910–4920.
Miranda, V., Krstulovic, J., Keko, H., Moreira, C., and Pereira, J. (2011). Reconstructing missing data in state estimation with autoencoders. IEEE Transactions on power systems, 27(2):604–611.
Singh, R., Pal, B., and Jabr, R. (2009). Choice of estimator for distribution system state estimation. IET generation, transmission & distribution, 3(7):666–678.
Soares, W., de Souza, J. C. S., Do Coutto Filho, M. B., and Augusto, A. A. (2019). Distribution system state estimation with real-time pseudo-measurements. In 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), pages 1–5. IEEE.
Vijayalakshmi, G. and Shanthakumar, M. (2019). Image restoration on fusion of mammographs and mri breast images using dual tree complex wavelet transform. International Journal of Advanced Science and Technology, 28(17):842–857. cited By 0.
Xu, W., Zhang, J., Li, X., Yuan, S., Ma, G., Xue, Z., Jing, X., and Cao, J. (2022). Intelligent denoise laser ultrasonic imaging for inspection of selective laser melting components with rough surface. NDT & E International, 125:102548.
Yilmaz, C. S., Yilmaz, V., and Gungor, O. (2022). A theoretical and practical survey of image fusion methods for multispectral pansharpening. Information Fusion, 79:1–43.
Zhang, X., Mu, J., Zhang, X., Liu, H., Zong, L., and Li, Y. (2022). Deep anomaly detection with self-supervised learning and adversarial training. Pattern Recognition, 121:108234.
Zhao, J., Huang, C., Mili, L., Zhang, Y., and Min, L. (2020). Robust medium-voltage distribution system state estimation using multi-source data. In 2020 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1–5.
Publicado
31/07/2022
Como Citar
SILVA, Luiz Phillip Q. da; SOUZA, Julio Cesar S. de; C. FILHO, Milton Brown Do.
O Uso de AutoEncoders como Fomentador de Medidas em Sistemas de Distribuição. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 49. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 128-139.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2022.223145.
