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Advanced Metering Infrastructure Data Driven Phase Identification in Smart Grid

Authors:
Wenyu Wang
Nanpeng Yu
Zhouyu Lu

Keywords: AMI; density-based clustering; phase identification; smart grid; t-SNE.

Abstract:
Many important distribution network applications, such as load balancing, state-estimation, and network reconfiguration, depend on accurate phase connectivity information. The existing data-driven phase identification algorithms have a few drawbacks. First, the existing algorithms require the number of phase connections as an input. Second, they can not provide accurate results when there is a mix of phase-to-neutral and phase-to-phase connected smart meters, or when the distribution circuit is less unbalanced. This paper develops an advanced metering infrastructure (AMI) data driven phase identification algorithm that addresses the drawbacks of the existing solutions in two ways. First, it leverages a nonlinear dimensionality reduction technique to extract key features from the voltage time series. Second, a constraint-driven hybrid clustering (CHC) algorithm is developed to dynamically create smart meter clusters with arbitrary shapes. The field validation results show that the proposed algorithm outperforms the existing ones. The improvement in the phase identification accuracy is more pronounced for distribution feeders that are less unbalanced. In addition, this paper discovers that more granular voltage time series leads to higher phase identification accuracy.

Pages: 16 to 23

Copyright: Copyright (c) IARIA, 2017

Publication date: September 10, 2017

Published in: conference

ISSN: 2519-8483

ISBN: 978-1-61208-588-3

Location: Rome, Italy

Dates: from September 10, 2017 to September 14, 2017