In a power distribution network, the network topology information is essential for an efficient operation. This network connectivity information is often not available at the low voltage (LV) level due to uninformed changes that happen from time to time. In this paper, we propose a novel data-driven approach to identify the underlying network topology for LV distribution networks including the load phase connectivity from time series of energy measurements. The proposed method involves the application of principal component analysis and its graph-Theoretic interpretation to infer the steady state network topology from smart meter energy measurements. The method is demonstrated through simulation on randomly generated networks and also on IEEE recognized Roy Billinton distribution test system. © 2010-2012 IEEE.

Ramkrishna Pasumarthy

Department of Electrical Engineering

Nirav Pravinbhai Bhatt

Department Of Biotechnology

Electric power distribution

Graph theory

Phase Meters

Principal component analysis

VOLTAGE DISTRIBUTION MEASUREMENT

Data-driven approach

DISTRIBUTION NETWORK TOPOLOGY

LOW VOLTAGE DISTRIBUTION NETWORK

LV DISTRIBUTION NETWORKS

Network connectivity

PHASE CONNECTIVITIES

Phase identification

Power distribution network

Smart meters

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data

IEEE Transactions on Smart Grid

Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact phase connectivity information is important for the efficient operation and control of distribution system. We propose a new data driven approach to the problem based on Principal Component Analysis (PCA) and its Graph Theoretic interpretations, using energy measurements in equally timed short intervals, generated from smart meters. We propose an algorithm for inferring phase connectivity from noisy measurements. The algorithm is demonstrated using simulated data for phase connectivities in distribution networks. © 2016 American Automatic Control Council (AACC).

Fulltext

Proceedings of the American Control Conference

A novel approach for phase identification in smart grids using Graph Theory and Principal Component Analysis

Data reconciliation (DR) and principal component analysis (PCA) are two popular data analysis techniques in process industries. Data reconciliation is used to obtain accurate and consistent estimates of variables and parameters from erroneous measurements. PCA is primarily used as a method for reducing the dimensionality of high dimensional data and as a preprocessing technique for denoising measurements. These techniques have been developed and deployed independently of each other. The primary purpose of this article is to elucidate the close relationship between these two seemingly disparate techniques. This leads to a unified framework for applying PCA and DR. Further, we show how the two techniques can be deployed together in a collaborative and consistent manner to process data. The framework has been extended to deal with partially measured systems and to incorporate partial knowledge available about the process model. © 2015 Elsevier Ltd.

Computers and Chemical Engineering

Deconstructing principal component analysis using a data reconciliation perspective

Journal	Data powered by TypesetIEEE Transactions on Smart Grid
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN	19493053
Open Access	No