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Anomaly Detection of Distribution Network Synchronous Measurement Data Based on Large Dimensional Random Matrix

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Advances in Artificial Systems for Medicine and Education II (AIMEE2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 902))

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Abstract

In order to identify faults of power system measurement subsystem, a method based on deep belief network is proposed in this paper. Firstly, data from actual measurement system is collected and divided into training and test samples. And then, the data is used to train a deep belief network. Finally, the model’s fault diagnosis results and actual samples’ labels are combined as a cross-validation set to test the deep belief network. The results show that the method based on deep belief network proposed in this paper can be more stable and reliable identification of electric power measurement equipment fault diagnosis.

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References

  1. Hurwitz, J.: The importance of monitoring all your smart meters. Electron. Eng. Prod. World 13(1), 23–24 (2016)

    Google Scholar 

  2. Xiao, Y., Jiang, B., Zhao, W., et al.: Research on IEC61850 based verification technologies of digital watt-hour meter. Electr. Metering Instrum. 7(4), 121–128 (2014)

    Google Scholar 

  3. Xu, W., Zhao, W., et.al.: A method for electrical energy measurement in consideration of inter-harmonics. Power Syst. Technol. (2016)

    Google Scholar 

  4. Solanics, P., Kozminski, K., Bajpai, M., et al.: The impact of large steel mill loads on power generating units. IEEE Trans. Power Deliv. 15(1), 24–30 (2000)

    Article  Google Scholar 

  5. Lin, G., Zhou, S., Sun, W., et al.: Calibration technology of nontraditional electric energy measuring equipment in digitalized substation. Proc. CSU-EPSA 23(3), 145–149 (2011)

    Google Scholar 

  6. Femine, A.D., Gallo, D., Landi, C., et al.: Advanced instrument for field calibration of electrical energy meters. IEEE Trans. Instrum. Meas. 58(3), 618–625 (2009)

    Article  Google Scholar 

  7. Wang, L., Lei, M., Zhang, S.: Research on electric energy algorithm by IEC 61850-9-1 protocol. Electr. Meas. Instrum. 49(2), 13–18 (2012)

    Google Scholar 

  8. Chen, R., Wang, Z., Kong, Z., et al.: Study of a new algorithm of digital wattmeter calibration. Electr. Meas. Instrum. 49(9), 18–23 (2012)

    Google Scholar 

  9. Salakhutdinov, R., Hinton, G.E.: Deep Boltzmann machines. In: AISTATS, vol. 1, p. 3 (2009)

    Google Scholar 

  10. Mohamed, A.R., Dahl, G.E., Hinton, G.: Acoustic modeling using deep belief networks. IEEE Trans. Audio Speech Lang. Proc. 20(1), 14–22 (2012)

    Google Scholar 

  11. Nair, V., Hinton, G.E.: 3D object recognition with deep belief nets. In: International Conference on Neural Information Processing Systems, Curran Associates Inc., pp. 1339–1347 (2009)

    Google Scholar 

  12. Zhou, S., Chen, Q., Wang, X.: Active deep learning method for semi-supervised sentiment classification. Neurocomputing 120(10), 536–546 (2013)

    Article  Google Scholar 

  13. Zhou, S., Chen, Q., Wang, X.: Fuzzy deep belief networks for semi-supervised sentiment classification. Neurocomputing 131(9), 312–322 (2014)

    Article  Google Scholar 

  14. Zaremba, W., Sutskever, I.: Learning to Execute. Eprint Arxiv (2015)

    Google Scholar 

  15. Yakkali, R.T., Raghava, N.S.: Neural network synchronous binary counter using hybrid algorithm training. Int. J. Image Graph. Signal Process. (IJIGSP) 9(10), 38–49 (2017). https://doi.org/10.5815/ijigsp.2017.10.05

  16. Al-Maqaleh, B.M., Al-Mansoub, A.A., Al-Badani, F.N.: Forecasting using artificial neural network and statistics models. Int. J. Educ. Manag. Eng. (IJEME) 6(3), 20–32 (2016). https://doi.org/10.5815/ijeme.2016.03.03

  17. Praynlin, E., Latha, P.: Performance analysis of software effort estimation models using neural networks. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 5(9), 101–107 (2013). https://doi.org/10.5815/ijitcs.2013.09.11

  18. Thu, T.N.T., Xuan, V.D.: Supervised support vector machine in predicting foreign exchange trading. Int. J. Intell. Syst. Appl. (IJISA) 10(9), 48–56 (2018). https://doi.org/10.5815/ijisa.2018.09.06

  19. Gopalan, N.P., Bellamkonda, S.: Pattern averaging technique for facial expression recognition using support vector machines. Int. J. Image Graph. Signal Process. (IJIGSP) 10(9), 27–33 (2018). https://doi.org/10.5815/ijigsp.2018.09.04

  20. Desai, P., Kulkarni, G.R.: Use of API’s for comparison of different product information under one roof: analysis using SVM. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 10(6), 11–22 (2018). https://doi.org/10.5815/ijitcs.2018.06.02

  21. Ahmad, M., Aftab, S.: Analyzing the performance of SVM for polarity detection with different datasets. Int. J. Mod. Educ. Comput. Sci. (IJMECS) 9(10), 29–36 (2017). https://doi.org/10.5815/ijmecs.2017.10.04

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Authors and Affiliations

  1. Guangdong Power Grid Co., Ltd. Foshan Power Supply Bureau, Foshan, 528200, Guangdong, China

    Zhongming Chen, Yaoyu Zhang, Chuan Qing, Jierong Liu, Jiaqi Tang & Jingzhi Pang

Authors
  1. Zhongming Chen
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  2. Yaoyu Zhang
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  3. Chuan Qing
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  4. Jierong Liu
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  5. Jiaqi Tang
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  6. Jingzhi Pang
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Corresponding author

Correspondence to Zhongming Chen .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute, Russian Academy of Sciences, Moscow, Russia

    Sergey V. Petoukhov

  3. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Davie, FL, USA

    Matthew He

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Chen, Z., Zhang, Y., Qing, C., Liu, J., Tang, J., Pang, J. (2020). Anomaly Detection of Distribution Network Synchronous Measurement Data Based on Large Dimensional Random Matrix. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education II. AIMEE2018 2018. Advances in Intelligent Systems and Computing, vol 902. Springer, Cham. https://doi.org/10.1007/978-3-030-12082-5_41

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