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Data-driven consumer-phase identification in low-voltage distribution networks considering prosumers

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Abstract

Knowing the correct phase connectivity information plays a significant role in maintaining high-quality power and reliable electricity supply to end-consumers. However, managing the consumer-phase connectivity of a low-voltage distribution network is often costly, prone to human errors, and time-intensive, as it involves either installing expensive high-precision devices or employing field-based methods. Besides, the ever-increasing electricity demand and the proliferation of behind-the-meter resources have also increased the complexity of leveraging the phase connectivity problem. To overcome the above challenges, this paper develops a data-driven model to identify the phase connectivity of end-consumers using advanced metering infrastructure voltage and current measurements. Initially, a preprocessing method that employs linear interpolation and singular value decomposition is adopted to improve the quality of the smart meter data. Then, using Kirchoff’s current law and correlation analysis, a discrete convolution optimization model is built to uniquely identify the phase to which each end-consumer is connected. The data sets utilized are obtained by performing power flow simulations on a modified IEEE-906 test system using OpenDSS software. The robustness of the model is tested against data set size, missing smart meter data, measurement errors, and the influence of prosumers. The results show that the method proposed correctly identifies the phase connections of end-consumers with an accuracy of about 98%.

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References

  1. Hashmi M U, Brummund D, Lundholm R, et al. Consensus based phase connectivity identification for distribution network with limited observability. Sustainable Energy, Grids and Networks, 2023, 34: 101070

    Article  Google Scholar 

  2. Gao J, Lu Y, Wu B, et al. Coordinated management and control strategy in the low-voltage distribution network based on the cloud-edge collaborative mechanism. Frontiers in Energy Research, 2022, 10: 903768

    Article  Google Scholar 

  3. Al-Jaafreh M A A, Mokryani G. Planning and operation of LV distribution networks: A comprehensive review. IET Energy Systems Integration, 2019, 1(3): 133–146

    Article  Google Scholar 

  4. Hoogsteyn A, Vanin M, Koirala A, et al. Low voltage customer phase identification methods based on smart meter data. Electric Power Systems Research, 2022, 212: 108524

    Article  Google Scholar 

  5. García S, Mora-Merchán J M, Larios D F, et al. Phase topology identification in low-voltage distribution networks: A Bayesian approach. International Journal of Electrical Power & Energy Systems, 2023, 144: 108525

    Article  Google Scholar 

  6. Tang X, Milanovic J V. Phase identification of LV distribution network with smart meter data. In: 2018 IEEE Power & Energy Society General Meeting. Portland: IEEE, 2018, 1–5

    Google Scholar 

  7. Matijašević T, Antić T, Capuder T. Voltage-based machine learning algorithm for distribution of end-users consumption among the phases. In: 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology. Opatija: IEEE, 2022, 974–979

    Google Scholar 

  8. Hosseini Z S, Khodaei A, Paaso A. Machine learning-enabled distribution network phase identification. IEEE Transactions on Power Systems, 2021, 36(2): 842–850

    Article  Google Scholar 

  9. Marrón L, Osorio X, Llano A, et al. Low voltage feeder identification for smart grids with standard narrowband PLC smart meters. In: 2013 IEEE 17th International Symposium on Power Line Communications and its Applications. Johannesburg: IEEE, 2013, 120–125

    Chapter  Google Scholar 

  10. Wen M H F, Arghandeh R, von Meier A, et al. Phase identification in distribution networks with micro-synchrophasors. In: 2015 IEEE Power & Energy Society General Meeting. Denver: IEEE, 2015, 1–5

    Google Scholar 

  11. Bindi M, Piccirilli M C, Luchetta A, et al. A comprehensive review of fault diagnosis and prognosis techniques in high voltage and medium voltage electrical power lines. Energies, 2023, 16(21): 7317

    Article  Google Scholar 

  12. Shen Z, Jaksic M, Mattavelli P, et al. Three-phase AC system impedance measurement unit (IMU) using chirp signal injection. In: 2013 28th Annual IEEE Applied Power Electronics Conference and Exposition. Long Beach: IEEE, 2013, 2666–2673

    Google Scholar 

  13. Luan W, Peng J, Maras M, et al. Smart meter data analytics for distribution network connectivity verification. IEEE Transactions on Smart Grid, 2015, 6(4): 1964–1971

    Article  Google Scholar 

  14. Yan Y, Zhou X, Bao W, et al. Connection identification of low voltage distribution areas based on distance measurement and trend similarity. In: 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference. Nanjing: IEEE, 2020, 1–5

    Google Scholar 

  15. Zhou L, Li Q, Zhang Y, et al. Consumer phase identification under incomplete data condition with dimensional calibration. International Journal of Electrical Power & Energy Systems, 2021, 129: 106851

    Article  Google Scholar 

  16. Izadi M, Mohsenian–Rad H. Improving real-world measurement-based phase identification in power distribution feeders with a novel reliability criteria assessment. In: 2021 IEEE PES Innovative Smart Grid Technologies Europe. Espoo: IEEE, 2021, 1–5

    Google Scholar 

  17. Chen K, Shi J, Wei X, et al. Phase identification with singlephase meter and concentrator based on NMF dimension reduction and label propagation. In: 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems. Jiaxing: IEEE, 2021, 1–6

    Google Scholar 

  18. Chiu J, Wong A, Park J, et al. Phase identification of smart meters using a Fourier series compression and a statistical clustering algorithm. In: 2022 IEEE Electrical Power and Energy Conference. Victoria: IEEE, 2022, 224–228

    Chapter  Google Scholar 

  19. Zaragoza N, Rao V. Phase identification of power distribution systems using hierarchical clustering methods. In: 2021 North American Power Symposium. College Station: IEEE, 2021, 1–6

    Google Scholar 

  20. Ma Y, Fan X, Tang R, et al. Phase identification of smart meters by spectral clustering. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration. Beijing: IEEE, 2018, 1–5

    Google Scholar 

  21. Peng Q, Liu X, Hu F, et al. Consumers’ phase identification in low voltage station area based on wavelet analysis of consumption data. In: 2021 IEEE International Conference on Power, Intelligent Computing and Systems. Shenyang: IEEE, 2021, 346–350

    Google Scholar 

  22. Zhou L, Zhang Y, Liu S, et al. Consumer phase identification in low-voltage distribution network considering vacant users. International Journal of Electrical Power & Energy Systems, 2020, 121: 106079

    Article  Google Scholar 

  23. Yi Y, Liu S, Zhang Y, et al. Phase identification of low-voltage distribution network based on stepwise regression method. Journal of Modern Power Systems and Clean Energy, 2023, 11(4): 1224–1234

    Article  Google Scholar 

  24. Kong X, Zhang X, Lu N, et al. Online smart meter measurement error estimation based on EKF and LMRLS method. IEEE Transactions on Smart Grid, 2021, 12(5): 4269–4279

    Article  Google Scholar 

  25. Khan M A, Hayes B P. A reduced electrically-equivalent model of the IEEE European low voltage test feeder. In: 2022 IEEE Power & Energy Society General Meeting. Denver: IEEE, 2022, 1–5

    Google Scholar 

  26. Ni F, Liu J Q, Wei F, et al. Phase identification in distribution systems by data mining methods. In: 2017 IEEE Conference on Energy Internet and Energy System Integration. Beijing: IEEE, 2017, 1–6

    Google Scholar 

  27. Dukhan M, Ablavatski A. Two-pass softmax algorithm. In: 2020 IEEE International Parallel and Distributed Processing Symposium Workshops. New Orleans: IEEE, 2020, 386–395

    Google Scholar 

  28. Wang W, Yu N. Maximum marginal likelihood estimation of phase connections in power distribution systems. IEEE Transactions on Power Systems, 2020, 35(5): 3906–3917

    Article  Google Scholar 

  29. Brunton S L, Kutz J N. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge: Cambridge University Press, 2019

    Book  Google Scholar 

  30. Zhu M, Ghodsi A. Automatic dimensionality selection from the scree plot via the use of profile likelihood. Computational Statistics & Data Analysis, 2006, 51(2): 918–930

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by Egypt-Japan University of Science and Technology and Japan International Cooperation Agency (JICA) under TICAD7 contract.

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Authors and Affiliations

  1. Electrical Power Engineering Department, Faculty of Engineering, Egypt-Japan University of Science and Technology (E-JUST), New Borg El Arab City, Alexandria, 21934, Egypt

    Geofrey Mugerwa, Tamer F. Megahed & Sobhy M. Abdelkader

  2. Electrical Engineering Department, Mansoura University, El-Mansoura, 35516, Egypt

    Tamer F. Megahed & Sobhy M. Abdelkader

  3. Electronics and Communications Engineering Department, Faculty of Engineering, Egypt-Japan University of Science and Technology (E-JUST), New Borg El Arab City, Alexandria, 21934, Egypt

    Maha Elsabrouty

Authors
  1. Geofrey Mugerwa
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  2. Tamer F. Megahed
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  3. Maha Elsabrouty
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  4. Sobhy M. Abdelkader
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Correspondence to Geofrey Mugerwa.

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Mugerwa, G., Megahed, T.F., Elsabrouty, M. et al. Data-driven consumer-phase identification in low-voltage distribution networks considering prosumers. Front. Energy (2024). https://doi.org/10.1007/s11708-024-0946-4

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  • DOI: https://doi.org/10.1007/s11708-024-0946-4

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