PV GENERATION MONITORING USING CALCULATED POWER FLOW FROM μPMUS

Autores

  • Lucas Tokarski Lima Universidade Federal do Paraná
  • Eduardo Parente Ribeiro Universidade Federal do Paraná
  • James Alexandre Baraniuk Universidade Federal do Paraná
  • Roman Kuiava Universidade Federal do Paraná

DOI:

https://doi.org/10.59627/cbens.2022.1064

Palavras-chave:

μPMU, power flow, microgrids, PV generation

Resumo

The use of PMUs (Phasor Measurement Units) to monitor microgrids has grown over the last years, due to its ability to offer accurate and synchronized voltage, current, and frequency measurements. In many microgrids, the PMUs operate without a current transformer (CT) and measure only voltage phasors values. We propose a power flow (PF) calculation using μPMU (or micro-PMU) voltage measurements, to allow these devices to indirectly monitor photovoltaic (PV) generation or electric loads. We used the μPMU data from a case study at the Centro Politécnico of the Universidade Federal do Paraná - UFPR campus, Brazil. We compared the calculated power flow with the power measured by a conventional power meter. We showed that this “virtual CT” approach with increased time resolution from μPMU can be particularly useful to aid in the detection of events, PV generation monitoring, and non-intrusive load monitoring (NILM) in general.

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Biografia do Autor

Lucas Tokarski Lima, Universidade Federal do Paraná

Universidade Federal do Paraná, Departamento de Engenharia Elétrica

Referências

H.-H. Chang, “Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses,” Energies, vol. 5, no. 11, pp. 4569–4589, 2012. [Online]. Available: https://www.mdpi.com/1996-1073/5/11/4569

G. Hart, “Nonintrusive appliance load monitoring,” Proceedings of the IEEE, vol. 80, no. 12, pp. 1870–1891, 1992.

E. C. Kara, C. M. Roberts, M. Tabone, L. Alvarez, D. S. Callaway, and E. M. Stewart, “Disaggregating solar generation from feeder-level measurements,” Sustainable Energy, Grids and Networks, vol. 13, pp. 112–121, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352467717301169

A. E. Lazzaretti, D. P. B. Renaux, C. R. E. Lima, B. M. Mulinari, H. C. Ancelmo, E. Oroski, F. Pöttker, R. R. Linhares, L. d. S. Nolasco, L. T. Lima, J. S. Omori, and R. B. d. Santos, “A multi-agent nilm architecture for event detection and load classification,” Energies, vol. 13, no. 17, 2020. [Online]. Available: https://www.mdpi.com/1996-1073/13/17/4396

L.-A. Lee and V. Centeno, “Comparison of μpmu and pmu,” in 2018 Clemson University Power Systems Conference (PSC), 2018, pp. 1–6.

K. Medidores, Kron Konect Data Sheet v2, Setember 2021. O. I. Elgerd, Electric Energy Systems Theory: An Introduction, 1971.

A. F. Moreno Jaramillo, D. M. Laverty, J. M. Del Rincón, P. Brogan, and D. J. Morrow, “Non-intrusive load monitoring algorithm for pv identification in the residential sector,” in 2020 31st Irish Signals and Systems Conference (ISSC), 2020, pp. 1–6.

G. H. C. Oliveira, R. Kuiava, G. V. Leandro, J. A. Vilela, R. De- monti, E. P. Ribeiro, J. S. Dias, E. M. S. Castro, and A. Pedretti, “Ufpr microgrid: A benchmark for distributed generation and energy efficiency research,” pp. 1–5, 2020.

V.Rathore and S. K. Jain, “Non intrusive load monitoring and load disaggregation using transient data analysis”, in 2018 Conference on Information and Communication Technology (CICT), 2018, pp, 1-5.

R. Saeedi, S. K. Sadanandan, A. K. Srivastava, K. L. Davies, and A. H. Gebremedhin, “An adaptive machine learning framework for behind-the-meter load/pv disaggregation,” IEEE Transactions on Industrial Informatics, vol. 17, no. 10, pp. 7060–7069, 2021.

M. Shahidehpour and J. F. Clair, “A functional microgrid for enhancing reliability, sustainability, and energy efficiency,” The Electricity Journal, vol. 25, no. 8, pp. 21–28, 2012. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S1040619012002278

F. Sossan, L. Nespoli, V. Medici, and M. Paolone, “Unsupervised disaggregation of photovoltaic production from composite power flow measurements of heterogeneous prosumers,” IEEE Transactions on Industrial Informatics, vol. 14, no. 9, pp. 3904–3913, 2018.

Y. Xingang, Z. Peng, D. Yang, P. Aiqiang, and X. Qin, “Nonint- tusive load monitoring and analysis based on power disturbance data,” in 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), 2020, pp. 2445–2449.

A. Zoha, A. Gluhak, M. A. Imran, and S. Rajasegarar, “Non- intrusive load monitoring approaches for disaggregated energy sensing: A survey,” Sensors, vol. 12, no. 12, pp. 16 841–, 2012. [Online]. Available: https://www.mdpi.com/1424-8220/12/12/16838

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Publicado

2022-08-16

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