Abstract
The rise in the accessibility of photovoltaic (PV) generators to consumers increases the possibility of reverse power flow (RPF) in the electric distribution system. RPF occurs when power flows to the design of the system. Overvoltage, power losses and protection system coordination are among the problems that could occur due to the presence of RPF. This paper describes an algorithm to detect the presence of RPF using optimally-placed micro-phasor measurement units (µPMUs) in the IEEE 34-Bus System with 5 PV generators. A machine learning algorithm based on a feedforward artificial neural network (ANN) was developed. The algorithm was able to detect the presence of RPF using (1) voltage and current and (2) polar- and (3) rectangular-impedance methods for training. The algorithm was also able to detect RPF under scenarios that were not used during the training process. Sensitivity analyses were performed for cases such as PV outage, PV relocation, PV addition, PV expansion and load increase. The susceptibility of the algorithm to true value errors (TVEs) was tested by adding error vectors on the µPMU measurements for both the training and testing populations.
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References
Mortazavi, H., Mehrjerdi, H., Saad, M., Lefebvre, S., Asber, D., Lenoir, L.: A monitoring technique for reversed power flow detection with high PV penetration level. IEEE Trans. Smart Grid 6, 2221–2232 (2015)
Antonova, G., Nardi, M., Scott, A., Pesin, M.: Distributed generation and its impact on power grids and microgrids protection. In: 2012 65th Annual Conference for Protective Relay Engineers, pp. 152–161 (2012)
Sarabia, A.: Impact of distributed generation on distribution system. Master’s thesis, Department of Energy Technology Aalborg University, Pontoppidanstraede 101, 9220 Aalborg East Denmark (2011)
Phadke, A.G., Thorp, J.S., Karimi, K.J.: State estimlatjon with phasor measurements. IEEE Trans. Power Syst. 1, 233–238 (1986)
von Meier, A., Culler, D., McEachern, A., Arghandeh, R.: Micro-synchrophasors for distribution systems. In: ISGT 2014, pp. 1–5 (2014)
Mabaning, A.A.G., Orillaza, J.R.C., von Meier, A.: Optimal PMU placement for distribution networks. In: 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), pp. 1–6 (2017)
Tahabilder, A., Ghosh, P.K., Chatterjee, S., Rahman, N.: Distribution system monitoring by using micro-PMU in graph-theoretic way. In: 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), pp. 159–163 (2017)
Al Rammal, Z., Abou Daher, N., Kanaan, H., Mougharbel, I., Saad, M.: Optimal PMU placement for reverse power flow detection, pp. 1–5 (2018)
Jamei, M., et al.: Anomaly detection using optimally placed µPMU sensors in distribution grids. IEEE Trans. Power Syst. 33, 3611–3623 (2018)
Yaghobi, H.: Fast predictive technique for reverse power detection in synchronous generator. IET Electr. Power Appl. 12(4), 508–517 (2018)
Sudhakar, P., Malaji, S., Sarvesh, B.: Reducing the impact of dg on distribution networks protection with reverse power relay. In: International Conference on Processing of Materials, Minerals and Energy. Materials Today: Proceedings, Ongole, Andhra Pradesh, India, 29th–30th July, vol. 5, no. 1, Part 1, pp. 51– 57 (2016)
De Carne, G., Buticchi, G., Zou, Z., Liserre, M.: Reverse power flow control in a st-fed distribution grid. IEEE Trans. Smart Grid 9, 3811–3819 (2018)
Lari, N.S., Abadeh, M.S.: Training artificial neural network by krill-herd algorithm. In: 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, pp. 63–67 (2014)
Hong, Z.: A preliminary study on artificial neural network. In: 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, vol. 2, pp. 336–338 (2011)
Kriesel, D.: A brief introduction to neural networks (2007)
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Eloja, P.J.P., Jorda, N.A.F., Pedrasa, M.A.A. (2020). Reverse Power Flow Detection Using Optimally Placed μPMUs in a Distribution System. In: Afonso, J., Monteiro, V., Pinto, J. (eds) Sustainable Energy for Smart Cities. SESC 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-030-45694-8_8
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DOI: https://doi.org/10.1007/978-3-030-45694-8_8
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