Structure Learning and Statistical Estimation in Distribution Networks - Part I

Deepjyoti Deka; Michael Chertkov; Scott Backhaus

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Structure Learning and Statistical Estimation in Distribution Networks - Part I

Deepjyoti Deka, Scott Backhaus, Michael Chertkov

Optimization and Control · Submitted 16 Jan 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our newly suggested algorithms apply to a wide range of realistic scenarios.

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arXiv ID1501.04131 (version 2)

Pith ID5SZHSKWK, Pith's permanent record of this paper and its review

CitationDeepjyoti Deka, Michael Chertkov, Scott Backhaus. "Structure Learning and Statistical Estimation in Distribution Networks - Part I." arXiv:1501.04131 (2015). https://pith.science/paper/5SZHSKWK

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What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.36 · human participants 0.02 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.12 · compares with prior results 0.07 · mentions code or data availability 0.02.

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Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient -- polynomial in time -- which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Traditionally power distribution networks are either not observable or only partially observable.	background 0.99	0.05	0.02	asserted 1.00
s1	This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring.	background 0.95	0.04	0.02	asserted 0.99
s2	In this two part paper, inspired by proliferation of metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment.	contribution 0.85	0.51	0.08	asserted 0.78
s3	In Part I, the objective is to learn the operational layout of the grid.	contribution 0.92	0.40	0.08	asserted 0.99
s4	Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure.	contribution 0.59	0.69	0.07	asserted 0.99
s5	Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets.	contribution 0.84	0.52	0.04	asserted 0.97
s6	Our newly suggested algorithms apply to a wide range of realistic scenarios.	contribution 0.82	0.77	0.10	asserted 1.00
s7	The algorithms are also computationally efficient -- polynomial in time -- which is proven theoretically and illustrated computationally on a number of test cases.	result 0.49	0.93	0.18	asserted 1.00
s8	The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.	contribution 0.52	0.73	0.18	asserted 0.87

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.60.

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full text
figures and tables
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S sections
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient -- polynomial in time -- which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

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How the literature uses it

8 arXiv papers cite this in 13 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 6 sentences without a confident label

Contests it 1 paper

Full availability of nodal measurements along with correlated power injections are the some of unrealistic assumptions in [ref] .

A Novel Topology Recovery Method for Low Voltage Distribution Networks · Introduction · contest 0.71

Uses its method or tool 1 paper

We can either use the utility records or the existing distribution grid topology estimation methods [ref] .

Fast Distribution Grid Line Outage Identification with $\mu$PMU · Only using $μ$PMUs data to identify line outages · 0.90

Cites it as background 5 papers

Linear Coupled (LC) model [ref] : In this model, the PF Eqs. ([ref]) are linearized jointly over voltage phase difference and magnitude deviations ($v_a -1=ε_a$) from the reference voltage, both of which are considered to be small.

Estimating Distribution Grid Topologies: A Graphical Learning based Approach · Distribution Grid: Structure and Power Flows · 0.81
There is a group of works that utilized statistical indexes-based power flow calculations to estimate the topology for distribution networks [ref] .

A Novel Topology Recovery Method for Low Voltage Distribution Networks · Introduction · 0.98
As generalizations of the DCPF, various linearized models of power flow have recently been proposed in several different coordinate systems [ref] .

Lossy DC Power Flow · Introduction · 0.85

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