A Comprehensive Evaluation of Supervised Machine Learning for the Phase Identification Problem

Authors: Brandon Foggo, Nanpeng Yu

Abstract:

Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: Distribution network, machine learning, network topology, phase identification, smart grid.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316877

References:

[1] C.-S. Chen, T.-T. Ku, and C.-H. Lin, “Design of phase identification system to support three-phase loading balance of distribution feeders,” IEEE Transactions on Industry Applications, vol. 48, no. 1, pp. 191–198, 2012.
[2] K. J. Caird, “Meter phase identification,” Mar. 27 2012, uS Patent 8,143,879.
[3] M. H. Wen, R. Arghandeh, A. von Meier, K. Poolla, and V. O. Li, “Phase identification in distribution networks with micro-synchrophasors,” in 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015, pp. 1–5.
[4] M. Dilek, R. P. Broadwater, and R. Sequin, “Phase prediction in distribution systems,” in Power Engineering Society Winter Meeting, 2002. IEEE, vol. 2, 2002, pp. 985–990.
[5] V. Arya, D. Seetharam, S. Kalyanaraman, K. Dontas, C. Pavlovski, S. Hoy, and J. R. Kalagnanam, “Phase identification in smart grids,” in Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on, Oct 2011, pp. 25–30.
[6] H. Pezeshki and P. J. Wolfs, “Consumer phase identification in a three phase unbalanced LV distribution network,” in 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Oct 2012, pp. 1–7.
[7] T. A. Short, “Advanced metering for phase identification, transformer identification, and secondary modeling,” IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 651–658, June 2013.
[8] W. Wang, N. Yu, B. Foggo, J. Davis, and J. Li, “Phase identification in electric power distribution systems by clustering of smart meter data,” in Machine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on. IEEE, 2016, pp. 259–265.
[9] W. Wang, N. Yu, and Z. Lu, “Advanced metering infrastructure data driven phase identification in smart grid,” GREEN 2017 Forward, pp. 16–23, 2017.
[10] C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.
[11] Y. Le Borgne, “Bias-variance trade-off characterization in a classification problem: What differences with regression,” Machine Learning Group, Univ. Libre de Bruxelles, Belgium, 2005.
[12] K. Hajebi, Y. Abbasi-Yadkori, H. Shahbazi, and H. Zhang, “Fast approximate nearest-neighbor search with k-nearest neighbor graph,” in IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, no. 1, 2011, p. 1312.
[13] W.-Y. Loh, “Classification and regression tree methods,” Encyclopedia of statistics in quality and reliability, 2008.
[14] B. P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu, and G. McGregor, “Boosted decision trees as an alternative to artificial neural networks for particle identification,” Nuclear Instruments and Methods in Physics Research A, vol. 543, pp. 577–584, May 2005.
[15] S. Sonoda and N. Murata, “Neural network with unbounded activation functions is universal approximator,” ArXiv e-prints, May 2015.
[16] D.-A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (ELUs),” ArXiv e-prints, Nov. 2015.
[17] G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-normalizing neural networks,” ArXiv e-prints, Jun. 2017.
[18] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, http://www.deeplearningbook.org.
[19] J. Lampinen and A. Vehtari, “Bayesian approach for neural networksreview and case studies,” Neural networks, vol. 14, no. 3, pp. 257–274, 2001.
[20] D. M. Blei, A. Kucukelbir, and J. D. McAuliffe, “Variational inference: A review for statisticians,” ArXiv e-prints, Jan. 2016.
[21] R. Ranganath, S. Gerrish, and D. Blei, “Black box variational inference,” in Artificial Intelligence and Statistics, 2014, pp. 814–822.
[22] Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in International Conference on Machine Learning, 2016, pp. 1050–1059.
[23] H. Lin and J. Bilmes, “How to select a good training-data subset for transcription: Submodular active selection for sequences,” Washington University Seattle Dept. of Electrical Engineering, Tech. Rep., 2009.
[24] U. Von Luxburg, “A tutorial on spectral clustering,” Statistics and computing, vol. 17, no. 4, pp. 395–416, 2007.
[25] A. Krause and D. Golovin, “Submodular function maximization.” 2014.