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6TH INTERNATIONAL CONFERENCE ON MODERN POWER SYSTEMS MPS2015, 18-21 MAY 2015, CLUJ-NAPOCA, ROMANIA
Data Mining Tools in Electricity Distribution Systems
Bogdan Constantin Neagu
Gheorghe Grigoraş
Power Systems Department
„Gheorghe Asachi” Technical University
Iaşi, Romania
bogdan.neagu@ee.tuiasi.ro
Power Systems Department
„Gheorghe Asachi” Technical University
Iaşi, Romania
ggrigor@ee.tuiasi.ro
different approaches such as fuzzy method [5], neural networks
[6], and genetic algorithm [7].
Abstract— Actually, the ongoing rapid growth of modern
computational technology has created an astounding flow of data.
The smart grids trends in Romania represent the Smart
Metering systems implementation. With the advent of Smart
Meter, a huge amount of power system data is being stored dayto-day in the DSO databases. The electricity consumption
information contains a lot of valuable knowledge, very useful for
both distribution system operator (DSO) and final users. In this
context, the smart meters are a key element for load controlling
and monitoring of the future smart grids. This paper describes
an approach to identify the consumption indicators using Data
Mining techniques. Knowledge discovery and machine learning
techniques can make use of this data to extract valuable
information and interesting patterns in these databases. The
originality of the paper consists in the data mining software
developed and controlled by a friendly graphic interface for
results visualization which can be used in a lot of electricity
distribution systems application. The analysis of study case
results can lead power consumers to use electricity rationally,
and provide decision support for each DSO.
The paper presents how load profiling and consumption
characteristic indicator extraction methodology can be
implemented to customers using the information provided by
smart meters. This methodology uses data mining techniques to
process data, identify real data, and generate typical load
profiles (TLPs). The major innovation of this paper consists in
the data mining software developed and controlled by a
friendly graphic interface for results visualization which can be
used in a lot of electricity distribution systems application. For
processing the large number of information provided by the
data mining software, our tool can be used.
The remainder of this paper is organized as follows. Section
2 presents a short presentation of smart meters. Section 3
presents the proposed data mining tools. Section 4 shows the
results of the data mining tools for a lot of substation, taking
into account large databases performed using smart meters.
Section 5 contains the paper conclusions.
Keywords—data mining; smart metering; distribution system;
I.
II.
INTRODUCTION
SMART METERING SYSTEMS
Nowadays power companies need extensive use of the
modern methods and technologies to offer better service to
their customers and respond to the needs of power industry.
The smart grids trends in Romania represent the Smart
Metering systems implementation. Smart meter is an advanced
energy meter that measures electrical energy consumption and
provides additional information as compared to a conventional
energy meter. It aims to improve the reliability, quality and
security of supply [8].
Presently, the rapid growth of modern computational
technology has created an astounding data flow. In this context,
the power system domain is facing an explosive growth of
data, and the power system development has resulted in more
and more real-time data being stored in databases [1].
The electricity consumption data contains a lot of valuable
knowledge, very useful for both distribution system operator
(DSO) and final users. In this context, the smart meters are a
key element for load controlling and monitoring of the future
smart grids. Many DSO are deploying smart metering systems
as a first level. Smart metering are an integral part of the smart
grid infrastructure in data collection and communications [2].
An important research topic used in optimal operation and
planning of distribution systems by DSO refers at customer’s
consumption. Also, the electricity consumption data contains a
lot of valuable knowledge, very useful for both distribution
system operator (DSO) and customers. In this context, the
smart meters are a key element for load controlling and
monitoring of the future smart grids. These systems have
emerged as a breakthrough in relationship between the final
consumer and DSO. Coupled with an Advanced Metering
Infrastructure (AMI) presented in Fig. 1 [9], Smart Metering
allows a large electricity distribution system application [10]. A
typical AMI network includes three main components: smart
meters on the consumer side; the communication network
between the smart meter and DSO; meter data management
application.
To extract useful information from a large databases, such
as for example power consumption, the data mining techniques
may be used [3]. The processing and interpreting this huge
volume of data is extremely complex, costly and time
consuming [4].
Taking into account that a rational data mining techniques
represents an essential instrument in infrastructure
development strategies for every DSO, in the paper a complex
data mining tools based on smart meters database are
presented. Regarding the data mining techniques used for
power system application, the recently literature indicate
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6TH INTERNATIONAL CONFERENCE ON MODERN POWER SYSTEMS MPS2015, 18-21 MAY 2015, CLUJ-NAPOCA, ROMANIA
Fig. 1. The AMI infrastructure for power distribution system
III.



DATA MINING TOOLS IN POWER DISTRIBUTION
SYSTEMS
Data mining is defined as “the nontrivial extraction of
implicit, previously unknown, and potentially useful
information from data” [11]. Also, data mining is the
process that extracts the information and knowledge which
is implicit in them, unknown in advance, but potentially
useful, from the massive, incomplete, fuzzy, noisy and
random data generated in the practical application [12].
Their techniques are different regarding the following
aspects: problem representation, accuracy, parameters that
must be optimized, results complexity, run time,
interpretability, transparency etc.
The proposed data mining software is divided into three
main modules [13]:
 The configuration module for select one or more
databases, each of them being defined through the name
of the consumption place.
 The monitoring module can perform a supervision of
the smart meter data both in real time (online
monitoring) or based on the history data (offline
monitoring). The monitoring functions are done for
time intervals established upon initialization by the
user. Also, this module allows the synchronization of
the counter timer with computer timer.
Data selection
 The data management and visualization module can
display different reports regarding the information
collected by smart meters, such as [14]: power
consumption on different points and selectable time
intervals (day, month, year), both tabular or graphically,
indicating the goal and peak load, the dispersion and the
average and hourly power factor or on selectable time
intervals: day, month, year; daily, monthly or yearly
balances from the monitored consumer; calculus and
display of the active, reactive or apparent load curves
characteristic parameters (peak value, goal value,
average quadratic load; peak load duration; load factor;
losses duration; loss factor; dispersion and standard
deviation; fill factor of the load curve; coefficient of
irregularity and shape factor of the load curve;
correlation coefficient between the active and reactive
loads etc.) for a certain interval selected by the user.
Data transformation
Data mining
Results interpretation and
validation
Incorporation of the discovered
information
Fig. 2. The steps for data mining process
The steps for data mining process are made in Fig. 2. An
important role plays the visualization, because it may
provide the preliminary data understanding or main specific
visualizations, presenting the obtaining results using the
data mining techniques. The data mining application in
electricity distribution system are multiple:



power forecasting;
consumption monitoring;
power networks planning etc.
Also, must be mentioned that for processing the large
number of information provided by the smart meters, a data
mining technique is used. This approach is used for power
consumption management and load profiling, taking into
account the uncertainty effect in the decision making
process.
dynamic security assessment;
adaptive (control and protection) system design;
load modelling and profiling;
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6TH INTERNATIONAL CONFERENCE ON MODERN POWER SYSTEMS MPS2015, 18-21 MAY 2015, CLUJ-NAPOCA, ROMANIA
IV.
d duration repported
reprresent the relaative value of the peak load
to whole
w
analysis period.
STUDY
T
CASE
T highlightinng the utility of
To
o proposed mining
m
data toools a
largee database waas used. The innformation froom this databaase is
recorded with thee smart meterrs in over 800 substations from
Molddavian area thhat supply witth electricity a lot of resideential
and tertiary
t
custom
mers.
In
I [15], for urban
u
MV/LV
V substation, the
t average filling
f
facto
ors are 0.644. In analyzeed case, onlly the resideential
custtomers can validate
v
this value, becau
use in the terrtiary
conssumption casee they have diffferent values,, Fig. 4.
B using the proposed
By
p
data mining tool all substations were
analyyzed taking into accouunt some chharacteristics. For
exam
mple, the substations supplly different customers suchh as:
residdential, hospital, domestic farm,
f
supermaarket, hotel, baakery
etc. Using
U
the metthodology preesented in [14]] and the propposed
data mining tool, the active tyypical load prrofile obtainedd for
diffeerent costumerr’s categories are presented in Fig.3.
T allure of the
The
t typical loaad profile has changed in reecent
yearrs being necesssary the DSO
O database update regardingg the
conssumption profi
files.
Fig. 4.
4 Four substatioon filing factor off active daily load
d curve
For
F
energy losses determ
mination in radial
r
distribbution
netw
works the lossees duration meethod is used. The loss factoor for
threee substations (bakery, supeermarket and hospital) from
m our
dataabase was anaalyzed. From Fig. 5 can bee observed thaat the
loss factor values estimated witth proposed data mining toool are
supeerior to those from our coountry literaturre [15]. It folllows
that the energy loosses values inn the low volltage networkks can
be overrated
o
or unnderestimated..
Fig. 3.
3 Typical load profile
p
determinattion using propossed data mining toools
T
Table
1 showss the characterristic parameteer (minimum load,
peakk load, averagge load, load duration
d
- Tmaxx, load factor T*
T max,
coeffficient of varriation - kV, nonuniformityy coefficient - α)
regaarding the actiive daily loadd curve for a hospital
h
consuumer
(worrking and weeekend day), extracted
e
direcctly from the data
miniing tools.
Table 1. Dailyy load curve charracteristic values extracted
e
from
d mining softw
data
ware for a hospitaal
Pmin [kkW]
Pmax [kkW]
Pmed [kkW]
Tmax [h]
[
T*maax
kV
α
Workking Day
18.476
53.759
31.871
144.240
0
0.593
0
0.366
0
0.344
Weeekend Day
17.952
34.962
23.202
15.987
0.666
0.187
0.515
Fig. 5.
5 Loss factor comparative analyysis of active daiily load curve forr three
substtation
Taking
T
into account the aforementioned, in ordeer to
estim
mate the pow
wer losses, neeed to have acccess to the onn-line
dataa about the substation
s
loaads in the peeriod under study.
s
How
wever, as it is not practical to measure so
s many substtation
load
ds, and we foound it better to estimate power
p
loss inn real
timee using the datta mining andd clustering tecchniques.
I the follow
In
wing, taking into account the consumpption
grow
wth, a comparrative analysiss between reaal (estimated using
u
the proposed datta mining toools) indicatorrs and those ones
propposed by otherr authors [15] at substation level
l
are madee.
CONCLLUSIONS
Taking
T
into account
a
that tthe data miniing techniquees are
wideely used todaay for the anaalysis of large datasets storred in
dataabases and daata archives, tthe proposed tools can be used
both
h by distributioon system opeerators and con
nsumers.
IIn Fig. 4 the filling factor of the active daily load cuurves
onlyy for four subsstations that suupply hospital, hotel, bakeryy and
residdential custom
mers are presennted. This facttor as known in
i the
literaature as smooothing coeffi
ficient of the load curve, and
211
6TH INTERNATIONAL CONFERENCE ON MODERN POWER SYSTEMS MPS2015, 18-21 MAY 2015, CLUJ-NAPOCA, ROMANIA
The proposed data mining tools are an obvious candidate
for assisting in such analysis of large scale power system
monitoring data. In the paper, significant results obtained from
cluster analysis, classification and association rules for
illustrate the applicability of data mining tools in power
distribution system have been developed.
[7]
[8]
[9]
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