1

Learning Topology of Distribution Grids using
only Terminal Node Measurements

arXiv:1608.05031v1 [math.OC] 17 Aug 2016

Deepjyoti Deka†, Scott Backhaus†, and Michael Chertkov†
†Los Alamos National Laboratory, USA
Email: deepjyoti@lanl.gov, backhaus@lanl.gov, chertkov@lanl.gov

Abstract—Distribution grids include medium and low voltage
lines that are involved in the delivery of electricity from
substation to end-users/loads. A distribution grid is operated
in a radial/tree-like structure, determined by switching on or
off lines from an underling loopy graph. Due to the presence
of limited real-time measurements, the critical problem of fast
estimation of the radial grid structure is not straightforward.
This paper presents a new learning algorithm that uses measurements only at the terminal or leaf nodes in the distribution
grid to estimate its radial structure. The algorithm is based on
results involving voltages of node triplets that arise due to the
radial structure. The polynomial computational complexity of
the algorithm is presented along with a detailed analysis of its
working. The most significant contribution of the approach is
that it is able to learn the structure in certain cases where available measurements are confined to only half of the nodes. This
represents learning under minimum permissible observability.
Performance of the proposed approach in learning structure is
demonstrated by experiments on test radial distribution grids.
Index Terms—Distribution Networks, Power Flows, Tree
learning, Voltage measurements, Missing data, Complexity

I. I NTRODUCTION
The power grid is operationally divided hierarchically into
transmission and distribution grids. While the transmission
grid connects the generators and includes high voltage lines,
the distribution grid comprises of medium and low voltage
lines that connect the distribution substation to the end
users/loads. Aside from low voltages, distribution grids are
structurally distinguished from the transmission side by their
operational radial structure. A distribution grid is operated
as a tree with a substation placed at the root node/bus that
is connected via intermediate nodes and lines to the terminal
end nodes/households. This radial operational structure is
derived from an underlying loopy network by switching on
and off some of the lines (network edges) [1]. The specific
structure may be changed from one radial configuration to
another, by reversing the switch statuses. Fig. 1 presents an
illustrative example of a radial distribution grid derived from
an underlying graph.
Historically, the distribution grid has had limited presence
of real time devices on the lines, buses and feeders [2].
Due to this, real-time monitoring of the operating radial
structure and the states of the resident buses on the distribution side is not straightforward. These estimation problems,
previously neglected, have become of critical importance due
to introduction of new devices like batteries and electric

vehicles, and intermittent generation resources like rooftop
solar panels. Optimal control in today’s distribution grid
requires fast topology and state estimation, often from limited
real-time measurements. In this context, it needs to be
mentioned that smart meters, micro-PMUs [3], frequency
measurements devices (FNETs) [4] and advanced sensors
(internet-of-things) capable of reporting real-time measurements are being deployed on the distribution side. However, in the current scenario, such devices are often limited
to households/terminal nodes as their primary purpose for
installation is services like price controllable demand and
load monitoring. A majority of the intermediate lines and
buses that connect the distribution substation to the terminal
nodes do not have real-time measurement devices and are
thus unobserved in terms of their structure and state. This
hiders real-time topology and state estimation.
This paper is aimed at developing a learning framework
that is able to overcome the lack of measurements at the intermediate nodes. Specifically, the primary goal of our work is
to propose an algorithm to learn the operating radial topology
using only real-time voltage magnitude measurements from
the terminal nodes. The reliance only on measurements from
terminal nodes is crucial as it makes our work applicable to
realistic deployment of smart devices in today’s distribution
grids. Further, our learning algorithm is able to tolerate a
much higher fraction of missing nodes (with unavailable
data) compared to prior work in this area. Our approach is
based on provable relations in voltage magnitudes of triplets
and pairs of terminal nodes that are used to discover the
operational edges iteratively. Computationally, the algorithm
has polynomial complexity in the number of nodes in the
system.
A. Prior Work
Topology learning in radial distribution grids has received
attention in recent times. Learning techniques in this area
vary, primarily based on the operating conditions and measurements available for estimation. The authors of [5] use
a Markov random field model for nodal phases to identify
faults in the grids. [6] uses conditional independence tests to
identify the grid topology in radial grids. [7] uses signs of
elements in the inverse covariance matrix of nodal voltages
to learn the operational topology. Signature/enevelope based
identification of topology changes is proposed in [8]. Such
comparison based schemes are used for parameter estimation

2

in [9], [10]. In contrast with nodal voltage measurements, line
flow measurements are used in a maximum likelihood based
scheme for topology estimation in [11]. In previous work
[1], [12], authors have analyzed topology learning schemes
that rely on trends in second moments of nodal voltage
magnitudes. Further, a spanning tree based topology learning
algorithm is proposed in [13]. This line of work [1], [12], [13]
is close in spirit to machine learning schemes [14], [15] developed to learn the structure of general probabilistic graphical models [16]. A major limitation of the cited literature is
that they assume measurement collection at most, if not all,
nodes in the system. To the best of our knowledge, work that
discuss learning in the presence of missing/unobserved nodes
[12], [13] assume missing nodes to be separated by greater
than two hops in the grid. As discussed earlier, distribution
grids often have real-time meters only at terminal nodes and
none at intermediate nodes that may be adjacent (one hop
away).
B. Contribution of This Work
In this paper, we discuss topology learning in the radial
distribution grid when measurements are limited to real-time
voltage readings at the terminal nodes (end-users) alone. All
intermediate nodes are unobserved and hence assumed to be
missing nodes. We analyze voltages in the distribution grid
using a linear lossless AC power flow model [1], [12] that is
analogous to the popular [17], [18] LinDistFlow equations.
For uncorrelated fluctuations of nodal power consumption,
we construct functions of voltage magnitudes at a pair or
triple of terminal nodes such that their values depend on
the edges that connect the group of nodes. These functions
provide the necessary tool to develop our learning scheme
that iteratively identifies operational edges from the leaf
onward to the substation root node. We discuss the computational complexity of the learning scheme and show that
it is a third order polynomial in the number of nodes. In
comparison to existing work, our approach is able to learn the
topology and thereby estimate usage statistics in the presence
of much greater fraction of missing/unobserved nodes. In
fact, in limiting configurations, our learning algorithm is able
to determine the true structure and statistics even when half
of the nodes are unobserved/missing. We demonstrate the
performance of our algorithm through experiments on test
distribution grids.
The rest of the manuscript is organized as follows. Section
II introduces notations used in the paper and describes the
grid topology and power flow models used for analysis in
later sections. Section III mentions assumptions made and describes important properties involving voltage measurements
at terminal nodes. Our algorithm to learn the operating radial
structure of the grid is discussed in Section IV with detailed
examples. We detail the computational complexity of the
algorithm in Section V. Simulation results of our learning
algorithm on test radial networks are presented in Section
VI. Finally, Section VII contains conclusions and discussion
of future work.

missing intermediate node

terminal node

Fig. 1. Radial distribution grid tree with substation/root node colored in
red. Dotted grey lines represent open switches. Terminal leaf nodes (d)
represent end-users from which real-time measurements are collected. The
intermediate missing nodes (b) are unobserved. Here, nodes a and c are
descendants of node a. Dashed lines represent the paths from nodes a and
d to the root node.

II. D ISTRIBUTION G RID : S TRUCTURE AND P OWER
F LOWS
Radial Structure: We denote the underlying graph of the
distribution grid by the graph G = (V, E), where V is the set
of buses/nodes and E is the set of all undirected lines/edges.
We term nodes by alphabets (a, b,...). An edge between nodes
a and b is denoted by (ab). An illustrative example is given
in Fig. 1. The operational grid consisting of one tree T with
nodes VT and operational edge set ET ⊂ E as shown in Fig. 1.
Our results are immediately extended to the case K > 1. In
tree T, Pa denote the set of edges in the unique path from
node a to the root node (reference bus). A node c is called
a descendant of node a if the path from node c to the root
passes through a, i.e., Pc ⊂ Pa . We use Da to denote the set
of descendants of a and include node a in Da by definition.
If edge (ac) ∈ ET and c is a descendant of a, we term a as
parent and c as its child node. Further, as discussed in the
Introduction, terminal nodes/leaf nodes in the tree represent
end-users or households that are assumed to be equipped with
real-time nodal meters. The remaining intermediate nodes
(not terminal nodes) are not observed and hence termed as
missing nodes. These definitions are illustrated in Fig 1. Next
we describe the notation used in the power flow equations.
Power Flow Models: We use zab = rab +ixab to denote the
complex impedances of line (ab) (i2 = −1) where rab and
xab represent the line resistance and reactance respectively.
Let real valued scalars, va , θa , pa and qa denote the voltage
magnitude, voltage phase, active and reactive power injection
respectively at node a. At each node a, Kirchhoff’s law of
power flow relate them as follows:
Pa = pa + iqa =

v2a − va vb exp(iθa − iθb )
z∗ab
b:(ab)∈ET

∑

(1)

Note that Eq. (1) is nonlinear and non-convex. Under realistic
assumption that losses of both active and reactive power
losses on each line of tree T is small, we can neglect second
order terms in Eq. (1) to achieve the following linearized
form [1], [7], [12]:

3

pa =

∑

(βab (θa − θb ) + gab (va − vb )) ,

(2)

∑

(−gab (θa − θb ) + βab (va − vb ))

(3)

b:(ab)∈ET

qa =

b:(ab)∈ET

.
where gab =

rab
, βab
2
2
xab + rab

.
=

xab
2
2
xab + rab

(4)

As shown in [1], Eqs. (2),(3) are equivalent to the LinDistFlow equations for power flow in distribution grid [17]–[19],
when deviations in voltage magnitude are assumed to be
small. Similar to LinDistFlow model, Eqs. (2),(3) are lossless
with sum power equal to zero (∑a∈VT Pa = 0). Further, note
that both active and reactive power injections are functions of
difference in voltage magnitudes and phases of neighboring
nodes. Thus, the analysis of the system can be reduced by
one node termed as reference node with voltage magnitude
and phase at all other nodes being measured relative to
this node. In our case, we take the substation/root node as
the reference node with voltage magnitude 1 and phase 0
respectively. The reference node’s injection also balances
the power injections in the remaining network. Inverting
Eqs. (2),(3) for the reduced system (without the reference
node), we express voltages as a function of nodal power
injections in the following vector form:
−1
−1
−1
−1
v = H1/r
p + H1/x
q θ = H1/x
p − H1/r
q
(5)
We term this as the Linear Coupled Power Flow (LCPF) model where p, q, v and θ are the vectors of real
power, reactive power injections, voltage magnitudes and
phase angles respectively at the non-substation nodes. H1/r
and H1/x are the reduced weighted Laplacian matrices for
tree T where reciprocal of resistances and reactances are used
respectively as edge weights. The reduction is achieved by
removing the row and column corresponding to the reference
bus in the original weighted Laplacian matrix. We denote
the mean of a random vector X by µX = E[X]. For two
random vectors X and Y , the covariance matrix is denoted by
ΩXY = E[(X − µX )(Y − µY )T ]. Using Eq. (5), we can relate
the means and covariances of voltage magnitudes with those
of active and reactive injection as follows:
−1
−1
µv = H1/r
µ p + H1/x
µq

(6)

−1
−1
−1
−1
−1
−1
+ H1/x
Ωq H1/x
+ H1/r
Ω pq H1/x
Ωv = H1/r
Ω p H1/r
−1
−1
+ H1/x
Ωqp H1/r

(7)

As considered in prior literature [7], [12], this assumption
is reasonable over short time-intervals where fluctuations in
nodal power usage at households/terminal nodes are independent and hence uncorrelated. Intermediate nodes that do not
represent end-users are uncorrelated if they have independent
usage patterns. Specifically, for intermediate nodes involved
in separation of power into downstream lines and without
any major nodal usage, the net power injection is contributed
by leakage or device losses and hence uncorrelated from
the rest. Note that Assumption 1 does not specify the class
of distributions that can model individual node’s power
injection. It is applicable when nodal injections are negative
(loads), positive (due to local generation) or a mixture of
both. In future work, we will relax this assumption and
discuss learning in the presence of positively correlated enduser injection profiles.
Next, we mention an analytical statement relating the
inverse of the reduced Laplacian matrix for a radial graph
that we use in our later results.
Lemma 1. [1], [20] The reduced weighted Laplacian matrix
H1/r for tree T satisfies
−1
H1/r
(a, b) =

∑T

(cd)∈Pa

rcd

(8)

Pb

−1
In other words, the (a, b)th entry in H1/r
is equal to the sum
of line resistances of edges common to paths from node a and
−1
b to the root. For example, in Fig. 1, H1/r
(a, d) = rbe + re0 .
Using Eq. (8) it follows immediately that if node b is the
parent of node a, then ∀c
(
rab
if node c ∈ Da
−1
−1
H1/r (a, c) − H1/r (b, c) =
(9)
0
otherwise,

Next, consider the function φ defined over two nodes a and
b as φab = E[(va − µva ) − (vb − µvb )]2 . φab represents the
variance of the difference in voltage magnitudes at nodes
a and b. Using Eq. (7) we can write φab in terms of power
injection statistics in tree T as follows
φab = Ωv (a, a) − 2Ωv (a, b) + Ωv (b, b)

(10)

−1
−1
= ∑(H1/r
(a, d) − H1/r
(b, d))2 Ω p (d, d)
d∈T
−1
−1
+ (H1/x
(a, d) − H1/x
(b, d))2 Ωq (d, d)



−1
−1
−1
−1
+ 2 H1/r
(a, d) − H1/r
(b, d) H1/x
(a, d) − H1/x
(b, d) Ω pq (d, d)
(11)

Using these statistical quantities, we discuss useful identities
that arise in radial distribution grids in the next section that
form the basis of our learning algorithms.

Note that Lemma 1 and Eq. (9) can be inserted in Eq. (11)
to simply it. In fact, doing so lets us derive properties of φ
for terminal nodes in tree T as discussed next.

III. P ROPERTIES OF VOLTAGE M AGNITUDES IN R ADIAL
G RIDS

Theorem 1. Let node b be the parent of nodes a and c in T
such that (ab) and (bc) are operational edges (see Fig. 2(a)).
Then

First, we make the following assumption regarding statistics of power injections at the non-substation grid nodes,
under which our results hold.
Assumption 1: Fluctuations of active and reactive powers at different nodes are uncorrelated. Thus, ∀a 6= b nonsubstation nodes, Ω p (a, b) = Ωq (a, b) = Ωqp (a, b) = 0.

2
2
φac = ∑ rab
Ω p (d, d) + xab
Ωq (d, d) + 2rab xab Ω pq (d, d)
d∈Da
2
2
+ rbc
Ω p (d, d) + xbc
Ωq (d, d) + 2rbc xbc Ω pq (d, d)
d∈Dc

(12)

= φab + φbc

(13)

∑

4

the root follow Pa ∩ Pd = Pb ∩ Pd . Using Lemma 1, we thus
−1
−1
have ∀d 6= a, b, H1/r
(a, d) = H1/r
(b, d) and
𝑘2
𝑘

𝑘

𝑟𝑎 2

𝑏

𝑐

𝑐

𝑎

(17)

−1
−1
−1
−1
(c, b) = rbk2
(b, b) − H1/r
(c, a) = rak2 , H1/r
(a, a) − H1/r
H1/r

𝐷𝑎
𝐷𝑐

(16)

−1
−1
−1
−1
(b, a) = rbk1
(b, b) − H1/r
(a, b) = rak1 , H1/r
(a, a) − H1/r
H1/r

As k1 and c are descendants of node k2 and Pk1 ∩ Pc = Pk2 ,

𝑘1
𝑎

−1
−1
−1
−1
(c, d)
(b, d) − H1/r
(c, d) = H1/r
(a, d) − H1/r
H1/r

𝑏

(18)

Further, using Eqs. (17, 18), we get
(a)
(b)
Fig. 2. (a) Distribution grid tree with nodes a and c as children of common
parent node b. (b)Terminal nodes a and b have common parent k1 . k1 and
k
terminal node c are descendants of k2 . ra2 is the sum of resistance on lines
(ak1 ), (k1 k) and (kk2 ) connecting nodes a and k.

Proof: Observe Lemma 1. As (ab) and (bc) are op−1
erational edges, the only nodes d such that (H1/r
(a, d) −
−1
H1/r (c, d)) 6= 0 are either descendants of a (set Da ) or
of c (set Dc ). Further Dc and Da are disjoint. When
−1
−1
d ∈ Da , (H1/r
(a, d) − H1/r
(c, d)) = rab , while when d ∈ Dc ,
−1
−1
(H1/r (a, d) − H1/r (c, d)) = −rbc . Using this in the formula
for φac in Eq. (11) gives us the relation. The equality
φac = φab + φbc is verified by plugging values in Eq. (11)
for φab , φbc and φac .
A few points are in order. First, note that the only
operational lines whose impedances appear on the right side
of Eq. (12) are (ab) and (bc). For the special case where a
and c are terminal nodes with parent b, the relation reduces
to the following:
2
2
φac = rab
Ω p (a, a) + xab
Ωq (a, a) + 2rab xab Ω pq (a, a)
2
2
+ rbc
Ω p (c, c) + xbc
Ωq (c, c) + 2rbc xbc Ω pq (c, c)

(14)

If the covariance of injections at a and b are known, the
above condition can be checked for each potential parent
node ‘b’ in linear time to identify the true parent. Second,
the equality in Eq. (13) is true only when Pa ∩ Pc = Pb . It is
replaced by a strict inequality for other configurations that we
omit discussing as they are outside the scope of our learning
conditions. The next theorem gives a result that relates the
voltages at three terminal nodes.
Theorem 2. Let terminal nodes a and b have common parent
node k1 . Let c be another terminal node such that c, k1 ∈
Dk2 and Pk1 ∩ Pc = Pk2 for some intermediate node k2 (see
Fig. 2(b)). Let rak2 and xak2 denote the sum of resistance and
reactance respectively on lines on the path from node a to
node k2 , i.e., rak2 = ∑ re f , xak2 = ∑ xe f . Define rbk2 , rkk12 etc. in
(e f )∈Pa −Pk2

the same way. Then

(e f )∈Pa −Pk2

φac − φbc = Ω p (a, a)((rak2 )2 − (rkk12 )2 ) + Ωq (a, a)((xak2 )2 − (xkk12 )2 )
+ Ω pq (a, a)(rak2 xak2 − rkk12 xkk12 ) − Ω p (b, b)((rbk2 )2 − (rkk12 )2 )
+ Ωq (b, b)((xbk2 )2 − (xkk12 )2 ) + Ω pq (b, b)(rbk2 xbk2 − rkk12 xkk12 )
(15)

Proof: As a and b are terminal nodes with same parent
k1 , for each node d 6= a 6= b, the paths from d, a and b to

−1
−1
−1
−1
(c, b) = rkk12
(a, b) − H1/r
(c, a) = H1/r
(b, a) − H1/r
H1/r

(19)

where rak2 , rkk12 etc. are defined in the statement of the
−1
theorem. Similar relations can be written for H1/x
terms as
well. We now expand φac and φbc using Eq. (11). Using
Eq. (16,18,19) in the expression for φac − φbc gives Eq. (15).
Observe that the lines whose impedances appear on the
right side of Eq. (15) are (ak1 ), (bk1 ) and the ones on the path
from node k1 to k2 . If this path is known till the penultimate
node k before k2 (see Fig. 2(b)), then Eq. (15) can be used
to learn edge (kk2 ) through a linear search among candidate
nodes for k2 . In the next section, we discuss the use of this
relation to learn the path from terminal pairs with common
parent (here a and b) to the root iteratively. Further, note
that the value of φac − φbc is independent of injections at
intermediate nodes and at terminal node c as long as c is
a descendant of k2 . Thus, Eq. (15) only helps identify c’s
relative position in the graph with respect to k2 . As shown
in the next section, we are able to locate c’s parent using a
post-order node traversal [21] in the grid graph.
IV. A LGORITHM TO LEARN TOPOLOGY USING TERMINAL
NODE MEASUREMENTS

Using the relations described in the previous section,
we discuss our algorithm to learn the operational topology of the radial distribution grid. As mentioned in the
Introduction, voltage measurements are available only at the
terminal/leaf nodes. The observer also has access to power
injection statistics (variances) at the terminal nodes. These
statistics are either known from historical data or computed
empirically from power injection measurements collected at
the terminal nodes. All intermediate nodes are missing and
their voltage measurements and injection statistics are not
observed/available. Further, we assume that impedances of
all lines (open or operational) in E for the underlying loopy
graph G are available as input. The task of the learning
algorithm is to identify the set ET of operational edges in the
radial grid T. The substation root node in tree T is assumed to
be connected to a single intermediate node. Otherwise each
sub-tree connected to the substation node can be considered
as a disjoint tree. First, we make the following restriction
regarding the degree of missing intermediate nodes.
Assumption 2: All missing intermediate nodes are assumed to have a degree greater than 2.
This assumption is necessary as without it, the solution
to the topology learning problem will not be unique. As

5

Fig. 3. Distribution grid tree where terminal node a is connected to node
d via two unknown intermediate nodes of degree 2. Either configuration
d, c, b, a or d, b, c, a for the intermediate nodes is a feasible structure given
no measurements at nodes b and c.

an example, consider the test distribution grid given in
Fig. 3 where the path from leaf node a to node d passes
through two intermediate nodes b and c of degree 2 each.
Both configuration A (operations edges (ab), (bc), (cd)) and
configuration B (operations edges (ac), (cb), (bd)) are feasible operational topologies if voltage and power injection
measurements are available only at the terminal node a. In
other words, different values of injection covariances at nodes
b and c can be taken such that relations between voltage
and injections are satisfied in either configuration. Similar
assumptions for uniqueness in learning general graphical
models are mentioned in [22]. For radial configurations that
respect Assumption 2, Algorithm 1 learns the topology using
measurements of voltage magnitudes and information of
injection statistics at terminal nodes.
Working: Algorithm 1 learns the structure of operational
tree T iteratively from leaf node pairs up to the root. Set
L represents the current set of terminal nodes/leaves with
unknown parents, while set M represents the set of all
missing intermediate nodes. Note that for each node a, para
denotes its identified parent while desa denotes a set of two
leaf nodes that are its descendants. Φ represents the empty
set. The two sets (par and des) are used to keep track of
the current stage of the learnt graph. There are three major
stages in the edge learning process in Algorithm 1.
First, we build edges between leaf nodes pairs a, c ∈ L
to their common missing parent node b in Steps 3 to 6.
Here the relation for φab derived in Theorem 1 and Eq. (14)
is used to identify the true missing parent in set M. Note
that only parents with two or more leaf nodes as children
can be identified at this stage. Parents that have at most one
leaf node as child (other children being missing intermediate
nodes) are not identified by this check.
Second, we remove nodes with discovered parents from
the set of leaf nodes L then iteratively learn the edges
between intermediate nodes in Steps 10 to 24. At each
stage of the iteration, M2 denotes the set of intermediate
nodes with unknown parents whose descendants (des) were
discovered in the previous iteration, while M1 denotes the
set of all intermediate nodes with unknown parent (par) and
known descendants. Clearly M2 ⊂ M1 . The parent k2 of each
node k in M2 is identified by using relation Eq. (15) in
Theorem 2 for its descendants a and b. There are two cases
considered for candidate parent k2 : (A) k2 belongs to M1
and has known descendant c (Step 15), and (B) k2 belongs
to M − M1 and has no discovered descendant (Step 18). For
the second case, the algorithm tries to find some leaf node
c that is potentially an undiscovered descendant of k2 by

Algorithm 1 Topology Learning using Terminal Node Data
Input: Injection covariances Ω p , Ωq , Ω pq at terminal nodes
L, Missing node set M = VT − L, m voltage magnitude
observations v for nodes in L, set of all edges E with line
impedances.
Output: Operational Edge set ET .
1:
2:
3:
4:
5:
6:
7:
8:
9:
10:
11:
12:
13:
14:
15:
16:
17:
18:
19:
20:
21:
22:
23:
24:
25:
26:
27:
28:
29:
30:
31:
32:
33:
34:
35:
36:

∀ nodes a, c ∈ L , compute φac = E[(va − µva ) − (vc − µvc )]2
∀a ∈ VT , define para ← Φ, desa ← Φ
for all a ∈ L do
if para = Φ&∃c ∈ L, b ∈ M s.t. φab , c satisfy Eq. (14) then
ET ← ET ∪ {(ab), (bc)}
para ← b, parc ← b, desb ← a, c
end if
end for
L ← {a : a ∈ L, para = Φ}, t p ← 1, M1 ← Φ
while t p > 0 do
M2 ← {k1 : k1 ∈ M − M1 , park1 = Φ, desk1 6= Φ}
M1 ← {k1 : k1 ∈ M, park1 = Φ, desk1 6= Φ}
for all k ∈ M1 with a, b ∈ desk do
k1 ← para
if ∃k2 ∈ M1 with M2 ∩ {k, k2 } 6= Φ with c ∈ desk2 , s.t.
φac − φbc satisfy Eq. (15) then
ET ← ET ∪ {(kk2 )}, park ← k2
else
if k ∈ M1 , ∃k2 ∈ M−M1 , c ∈ L, s.t. φac −φbc satisfy
Eq. (15) then
ET ← ET ∪ {(kk2 )}, park ← k2 , desk2 ← desk
end if
end if
end for
t p ← |{k1 : k1 ∈ M1 , park1 6= Φ}|
end while
if then|M1 | = 1
Join k ∈ M1 to root
end if
Form a post-order traversal node set W using para for
a : ∀a ∈ M, desa 6= Φ
for all c ∈ L do
for j ← 1 to |W| do
k2 ← W( j) with a, b ∈ desk2 , k1 ← para
if φac − φbc satisfy Eq. (15) then
ET ← ET ∪ {(ck2 )}, W ← W − {k2 }, j ← |W|
end if
end for
end for

checking for the relation φac − φbc specified in Eq. (15). If
the relation holds, node k2 is denoted as the parent of k1 . As
it is not clear if leaf c is an immediate descendant (child) of
k2 , no edge to c is added. The iterations culminate when no
additional edge between intermediate nodes is discovered.
Third, in Steps 29 to 36, the parents of the remaining leaf
nodes in L are identified. Note that Eq. (15) also holds for
non-immediate descendant c of k2 , hence it cannot be used
directly to identify c’s location. To overcome this, for each
c in L, we first check at descendants of k2 before k2 . This
is ensured by creating a post-order traversal list W [21] in
Step 28. In post-order traversal W, each potential parent is
reached only after all its descendants have been reached. The
node k2 which satisfies Eq. (15) in Step 32 now gives the true
parent of leaf c. The algorithm terminates after searching for
all leaves in L.

6

1

2

3

Fig. 4. Sequence in which edges area learnt by Algorithm 1 for an example
radial grid using only measurements at leaf nodes. In the first step, leaf pairs
with common parent are learnt, then intermediate nodes appear and finally
connections to leaf nodes with no leaf sibling are discovered.

(a)
(b)
Fig. 5. Cases where fraction of missing nodes becomes 50% (a) A binary
distribution grid tree with each intermediate node having degree 3. (b)
Distribution grid tree with each intermediate node with one child

Note: Empirically computed second moments of voltages
may differ from their true values and hence may not satisfy
relations from Theorems 1 and 2 exactly. Hence we use
tolerances τ1 and τ2 to check the correctness of Eq. (14) and
Eq. (15) respectively in Algorithm 1. We defer the selection
of optimal thresholds to future work.
We discuss the operation of Algorithm 1 through a test
example given in Fig. 4. As described in the previous paragraph, in the first step, the common parents of leaf node pairs
are discovered. In the next step, edges between intermediate
nodes are discovered iteratively. Finally, positions of leaf
nodes that do not have common parent with any other leaf
node are located by a post-order traversal and then the
substation connects to the node with no parent.

depth d −1 are missing. The second graph has the structure of
a line graph with one additional leaf node connected to each
intermediate node on the line. In either graph, as the number
of nodes increases, the fraction of missing nodes increases to
50%. In other words, configurations exist such that Algorithm
1 is able to learn the grid structure with approximately half
the nodes missing/unobserved.
This is crucial because 50% fraction of missing nodes
represents the threshold beyond which state estimation is not
possible even in the presence of knowledge of the operational
topology. This is because the LC-PF Eqs. (4) cannot be
solved if less than half the nodes are observed.

V. C OMPUTATIONAL C OMPLEXITY AND P ERFORMANCE

VI. E XPERIMENTS

Consider the original loopy graph G to be complete (all
node pairs belong to E). We compute the computational
complexity of operational tree in terms of the number of
nodes N = |VT |.
Algorithm Complexity: To measure the computational
complexity, we focus on the iteration steps. Let the number
of leaves in tree T be l0 . Detecting edges between leaf pairs
and their common parent (Steps 3 to 6) takes O(l02 (N − l0 ))
comparisons, which takes the form O(N 3 ) in the worst case.
Next we analyze the complexity of identifying edges between
intermediate nodes. Let the number of ‘new’ intermediate
nodes with unknown parents (set M2 ) in each iteration of
(Steps 10 to 24) be li . Each ‘new’ node is compared with
the set of all intermediate nodes with unknown parents
(M1 ⊇ M2 ) first. As addition of a ‘new’ node leads to
removal of its children (≥ 1) from M1 , the size of M1 never
increases more than its initial value (≤ l0 /2) giving this step
a worst case complexity of O(li l0 ). Comparison of nodes in
Mi with missing nodes in M − M1 and leaf nodes in L (Step
18) has worst case complexity O(li l0 (N − l0 )). Using l0 as
O(N) and ∑ li = O(N), the computational complexity of all
iterations in Steps 10 to 24 is thus O(N 3 ). Finally composing
the post-order tree traversal and checking at node location
(O(N)) for each leaf (O(N)) has complexity less than or
equal to O(N 2 ). The overall complexity of the algorithm is
thus O(N 3 ).
Maximum Missing Node Fraction: Consider the two
radial graphs given in Fig. 5 that satisfy the condition in Assumption 2. The first is a binary tree of depth d. All nodes till

Here we demonstrate performance of Algorithm 1. We
consider a radial network [23], [24] (Fig. 6) with 20
nodes and one substation. Out of those 20 nodes, 12 are
terminal/leaf nodes while 8 are intermediate nodes which
are missing. In each of our simulation runs, we construct
complex power injection samples at the non-substation nodes
from a multivariate Gaussian distribution that is uncorrelated
between different nodes as per Assumption 1. Next we use
LC-PF Eqs. (5) to generate nodal voltage magnitude measurements. To understand the performance of our algorithm,
we introduce 30 additional edges (at random) to construct the
loopy edge set E. The additional edges are given impedances
comparable to those of operational lines. The input to the
observer consists of voltage magnitude measurements and
injection statistics at the terminal nodes and impedances of
all the lines within the loopy edge set E. The average fraction
errors (number of errors/size of the operational edge set) in
reconstructing the grid structure is shown in Fig. 7(a) for
different sizes of terminal voltage magnitude measurements
used. Different curves in the plot depict different values of
tolerances τ1 and τ2 to check the correctness of Eq. (14) and
Eq. (15) in Algorithm 1. Note that the average fractional
errors decreases steadily with increase in the number of
measurements as empirical second moment statistics used
in Algorithm 1 get more accurate. The values of tolerance
to achieve the most accurate results are selected manually.
We plan to develop a theoretical selection of the optimal
tolerance values in future work.

7

sample complexity of the learning algorithm and optimizing
the selection of tolerance values based on the number of
samples are directions of our future research in this area.
Further, we plan to expand this learning regime to cases with
correlated injections.
R EFERENCES
Fig. 6. Layouts of the grid tested with 20 non-substation nodes. Black
lines represent operational edges. Some of the additional open lines (actual
number 30) are represented by dotted green lines.
0.45
τ =3.8*10−2, τ = 2*10−1
1

2

τ = 3.5*10−2, τ =2*10−1
1

0.4

2

−2

−1

τ1 = 3.5*10 , τ2 =1.5*10

Average fraction errors
in topology learning

−2

τ1 =3.2*10 , τ2 =1.5*10−1

0.35

τ1 = 3*10−2 , τ2 =1*10−1

0.3

0.25

0.2

0.5

1

1.5
2
Number of voltage measurement

2.5

3
4

x 10

(a)
Fig. 7. Average fractional errors in learning operational edges vs number
of samples used in Algorithm 1 for different values of tolerances τ1 and τ2 .

VII. C ONCLUSIONS
Topology learning is an important problem in distribution
grids as it influences several other control and managements
applications that form part of the smart grid. The learning
problem is complicated by the low placement of real-time
measurements in the distribution grid, primarily being placed
at terminal nodes that represent end-users or households. In
this paper, we study the problem of learning the operational
radial structure from a dense underlying loopy grid graph
in this realistic regime where only terminal node (end-user)
voltage measurements and injection statistics are available
as input and all other nodes are unobserved/missing. We use
a linear-coupled power flow model and show that voltages
terminal node pairs and triplets satisfy relations that depend
on their individual injection statistics and impedances on the
lines connecting them. We use these properties to propose
a learning algorithm that iteratively learns the grid structure
from the leaves onward towards the substation root. We show
that the algorithm has polynomial time complexity comparable to existing work, despite being capable of tolerating much
greater and realistic fraction of missing nodes. For specific
cases, this algorithm is capable of learning the structure with
50% unobserved nodes, beyond which state estimation is not
possible even in the presence of topology information. We
demonstrate performance of the learning algorithm through
experiments on distribution grid test case. Computing the

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