Power Theft Detection in Microgrids

Aryadevi Remanidevi Devidas and Maneesha Vinodini Ramesh

Amrita Center for Wireless Networks & Applications, AMRITA Vishwa Vidyapeetham (Amrita University), Kerala, India

Keywords: Electrical Grid, Micro-grid, Power Theft, Wireless Networks.

Abstract: Theft of electricity amounts to 1.5% GDP, of most of the developing nations like India. Hence there is a

great need to detect power thefts in developing nations. In this paper, we have proposed a wireless network

based infrastructure for power theft detection which caters to other functional requirements of the microgrid

such as renewable energy integration, automatic meter reading etc. Algorithm for power theft detection

(PTDA) which is proposed in this paper, works in the distributed intelligent devices of the microgrid

infrastructure for power theft detection. The coordinated action of intelligent devices with PTDA in the

microgrid infrastructure enables not only the detection of power theft, but the localization of power theft in

the micro-grid. PTDA increases the 1) cost of communication 2) energy consumption of intelligent devices

3) packet latency, if any critical data is piggy backed with power theft data in micro-grid. To solve these

issues, we have proposed EPTDNA (Efficient Power Theft Data Networking Algorithm) which uses the

frequency of power theft detection and average power draw for power theft, for the efficient routing of

power theft. The performance analysis and results given in this paper shows how EPTDNA solves the major

issues with PTDA.

1 INTRODUCTION

Smart Grid is the new generation electric grid

technology whose pivotal network is a wireless

network (Amin and Wollenberg, 2005). Microgrids

are small smart power grids, which is a part of smart

distribution grid, that can operate in islanded mode

or in grid-connected mode. The existing grid system

in developing nations is suffering due to a lot of

problems which affects the country’s economic

growth, one of which is power theft (Farhangi,

2010). The microgrid technology solves all these

problems to some extent. The features of microgrid

include, distributed generation of electricity,

integration of renewable energy source, power theft

detection, line fault detection, self-healing, advanced

metering infrastructure, and automated billing and

controlling (Hartono, Budiyanto and Setiabudy,

2013). The research paper (Myoung, Kim and Lee,

2010) introduces KEPCO's field area network

(FAN) architecture and research for smart

distribution automation system (DAS) and advanced

metering infrastructure (AMI). However, the current

research work in the smart grid or microgrid area

does not fully support real-time, reliable

communications and necessary smart grid services.

Power theft is considered as a bane of the power

grid in most of the developing nations. Nearly, 30%

of the generated electricity has not been billed in

these nations because of the power theft losses and

transmission losses. Hence there is an urgent need

from the utility side to detect and locate the power

theft. In this paper, we have proposed a wireless

based microgrid infrastructure for power theft

detection. The power theft detection algorithms

(PTDA) inside the intelligent devices in the

microgrid infrastructure detect and localize the

power theft in the grid. In PTDA, the messages

regarding the current draw or injection at each

intelligent device are transmitted to next nearby

node very often. This introduces three issues

namely, 1) cost of communication increases with

increase in number of message transfer, if the

communication module is not using ISM band, 2)

even if, SMs and SDNs are low powered devices,

full time switch on of these devices may contribute

to the un-sustainability of the microgrid in terms of

energy, since they are powered by micro-grid, 3) if

the energy consumption data from the consumers are

piggy backed with the multi-hop current flow data

for power theft detection, then it may introduce large

latency and will affect the billing process. To solve

342

Remanidevi Devidas A. and Vinodini Ramesh M..

Power Theft Detection in Microgrids.

DOI: 10.5220/0005446703420349

In Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS-2015), pages 342-349

ISBN: 978-989-758-105-2

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

these issues we have proposed an algorithm called

Efficient Power Theft Data Networking Algorithm

(EPTDNA) which works with PTDA.

The major contributions of this research paper

are:

1. Microgrid infrastructure for power theft

detection which caters to other functional

requirements of micro-grid.

2. Power theft detection algorithm (PTDA)

which uses Kirchhoff’s Current Law (KCL), for the

detection and localization of power theft.

3. Efficient Power Theft Data Networking

Algorithm (EPTDNA) for efficient routing of power

theft data, which solves three issues of PTDA.

The rest of the paper is organized as follows:

Section 2 discusses the related works and Section 3

gives a wireless networks based microgrid

infrastructure for power theft detection. Section 4

describes the power theft detection algorithm

(PTDA). Section 5 describes the problems of PTDA

and Efficient Power Theft Data Networking

Algorithm (EPTDNA) which solves the issues of

PTDA. Section 6 describes the performance

evaluation and results which shows how EPTDNA

solves the issues with PTDA which follows

conclusion in section 7.

2 RELATED WORKS

In most of the developing nations power theft is

happened by drawing power from the overhead grid

lines which are bare conductors. Usually, the

unauthorized consumers who are committing the

theft may not have a smart meter associated to them.

Most of the research papers addressed the power

theft committed by several cyber attacks.

The reference publication (Mashima and

Cardenas, 2012), the authors proposes the first threat

model for the use of data analytics, to detect the

anomalies of the data received from advanced

metering infrastructure. The reference paper

(Nikovski, Wang, Esenther, Sun, Sugiura, Muso and

Tsuru, 2013) proposes a method for power theft

detection based on the data collected by the smart

meters in the smart grid. They used predictive

models for calculating the technical losses in

electrical distribution networks and used these

models for power theft detection. The dissertation

(Depru, 2012) presents an encoding and

classification scheme that simplifies the energy

consumption pattern and maps them to the

corresponding irregularities in the consumption. The

reference paper (Devidas and Ramesh, 2010)

describes the method for detection and localization

of power theft in smart grids. But it has not

considered the bidirectional electricity flow in

microgrids. In this paper, we have proposed

algorithms for power theft detection in micro-grids

and for efficient routing of power theft data.

The research papers (Salinas, Li and Li, 2012),

(Weckx, Gonzalez, Tant, Rybel, Driesen, 2012) and

(McLaughlin, Holbert, Zonouz and Berthier, 2012)

deals with the power theft created due to several

cyber attacks. Solving of power theft by placing

intelligent devices throughout the power grid and by

computerised billing systems is explained in the

reference paper (Amarnath, Kalaivani and Priyanka,

2013). It deals with the power theft due to smart

meter tampering whereas our work deals with the

power theft due to distribution line tampering, the

kind of power theft that experiencing in developing

nations.

3 MICROGRID

INFRASTRUCTURE FOR

POWER THEFT DETECTION

Figure 1: Microgrid infrastructure for power theft

detection.

Microgrid infrastructure for power theft detection

involves power grid and an overlay communication

network. The detection of power theft is possible by

integrating intelligent communication agents at

different locations of the power grid. For power theft

detection intelligent communication infrastructure

are placed near all consumers and also on top of

each and every pole. Such a microgrid is shown in

the figure 1. Point of Common Coupling (PCC) is

the point in the power grid at which the microgrid is

PowerTheftDetectioninMicrogrids

343

connected to the main grid.

The intelligent devices near the consumers are

called Smart Meters (SMs), the intelligent devices

on top of all the poles are called Smart Distribution

Nodes (SDNs) and the intelligent devices associated

to the renewable energy sources are called

Renewable Energy Intelligent Device (REID).

Microgrid Control Station (MCS) is the main control

station of the micro-grid. SMs and SDNs measure

the current flow towards or away from each

consumer and distribution pole. SMs and SDNs have

the capability to send data to other SMs or SDNs or

MCS. These intelligent communication agents

consists of the current sensors for measuring

bidirectional current flow, microcontroller for

processing data and communication module for

sending the data to other intelligent agents. The

MCS controls all the intelligent agents and takes

care of the energy management inside the micro-

grid.

The communication technology used for the

transmission of data mainly depends on the

transmission range, data rate and transmission cost.

In India the maximum distance allowed between two

transmission line posts in a secondary distribution

grid is in the range of 40 to 50meters and the power

theft data is not a huge sized data. Hence the

recommended communication technologies for the

intelligent agents for power theft detection are

Zigbee or Wifi (Agarwal, Agarwal, Vyas and

Sharma, 2013) (Datar, 2008). Even though, these are

the recommended technologies for power theft

detection functionally of microgrid, depending on

other functionality of micro-grid, there can be a

change in the communication technologies used by

intelligent agents in the micro-grid.

4 POWER THEFT DETECTION

ALGORITHM (PTDA)

The SMs and SDNs in the microgrid infrastructure

are responsible for the detection of power theft in a

microgrid. Assume ‘SDN

’ is the k

SDN in the

microgrid ‘M’. Let ‘n’ be the number of direct

descendants (SMs and SDNs) of ‘SDN

’ as shown in

figure 2(a). ‘SDN

’ measures the current flow

through it using Kirchhoff’s Current Law (KCL).

Let ‘i

’ be the measured current by ‘SDN

’.

‘SDN

’ will also receives the current values with

the direction information from all its descendants. It

will sum all the current values got from its

descendants. Let ‘i

’ be the summation of

descendants current values.

Figure 2(a): ‘n’ descendants connected to the SDN ‘K’.

Figure 2(b): Tree-like topology of microgrid and MGAs

inside the micro-grid.

(1)

If i

≠i

, then ‘SDN

’ decide a power theft has

occurred and send the power theft information to the

MCS. The Power Theft flag (PT

flag

) will set True or

False based on the detection of power theft in the

line segment between ‘SDN

’ and its descendants.

CP and PT represents data packets with current

value and the power theft values respectively. The

algorithm for detecting the power theft in ‘SDN

’ is

as follows:

1. Measure i

using KCL.

2. Send CPmsg{src

, ,i

, time_stamp,dst

}

to next intelligent device.

3. Wait(t<T

sec

) until

4. ReceiveCP

,CP

,...,CP



5. if(!ReceiveCP

,CP

,...,CP

&&t>T

sec

)

6. Discard the CP

,CP

,...,CP

7. After ‘m’ number of rejection of CPs,

8. Send message to MCS

9. Initiate communication network

reorganization

10. Go to step2.

11. end if

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344

12. else if((ReceiveCP

,CP

,...,CP

&&t<T

sec

)

13. Extract current values i

, i

….i

14. Insert sign according to the direction

information,

15. i

16. Compare(i

, i

17. if(i

==i

-Tx

loss

Err

val

)

18. PT

flag

= false

19. end if

20. elseif(i

!= i

-Tx

loss

Err

val

)

21. PT

flag

= true

22. Send PTmsg{src

dir

, PT

flag

= true,

time_stamp,mcs

} to MCS.

23. end if

24. end if

The SDN measures the current flow through it

and wait T

sec

until it receives the current values of its

‘n’ descendants. If the current values from all ‘n’ are

not received till the time T

sec

, then the SDN discards

all the values and measures again the current value.

After certain number of such rejection of data, SDN

realizes a failure in the communication link or in the

descendant node. Then SDN will either initiate

reorganization of communication network or send a

message to MCS regarding the issue. When the

electric current travels through the distribution line,

it experiences some loss due to heat which is termed

as copper loss. The Tx

loss

represents this copper loss.

The current value that receives can be erroneous due

to electromagnetic interference. The maximum

allowed error is

Err

val

. CP is the current data

packet which contains the source ID, direction

vector, current value, time stamp and destination ID.

PT is the power theft data packet which contains

source ID, direction vector of power theft current

dir

), power theft current value (i

), power theft

flag, time stamp and the ID of MCS. The packet

structures of CP and PT messages are shown in

figure 3(a) and figure 3(b).

Figure 3(a): Structure of current packet.

Figure 3(b): Structure of power theft packet.

5 EFFICIENT NETWORKING OF

POWER THEFT DATA

In the power theft detection method (PTDA)

described in section 4, the messages regarding the

current draw or injection at each node are

transmitted to next nearby node very often. This

introduces three issues namely, 1) cost of

communication increases with increase in number of

message transfer, if the communication module is

not using ISM band (Baker, King and Welch, 2004),

2) even if, SMs and SDNs are low powered devices,

full time switch on of these devices may contribute

to the un-sustainability of the microgrid in terms of

energy, since they are powered by microgrid, 3) if

the energy consumption data from the consumers are

piggy backed with the multi-hop current flow data

for power theft detection, then it may introduce large

latency and will affect the billing process. To solve

these issues two questions need to be answered,

a) How often we need to check for power theft

inside a microgrid?

b) How to determine whether the data has to hop

through each and every intelligent device?

We propose an algorithm called Efficient Power

Theft Data Networking Algorithm (EPTDNA)

which works with PTDA to solve the above

mentioned issues.

For the efficient networking, assume the

microgrid has a tree-like topology as shown in figure

2(b), in which SMs and SDN are considered as

nodes in the topology. The independent sub-

branches of the microgrid tree are identified as

different Microgrid Areas (MGAs) such as

Frequently Identified MGA (FMGA), Occasionally

Identified MGA (OMGA) and Extremely Unlikely

Identified MGA (EUMGA) based on the full load in

the sub-branches, total generation capacity in the

sub-branches and the geographical terrain as shown

in figure 2(b). The Microgrid Controlling Station

(MCS) will find out the critical microgrid areas from

the identified MGAs based on the frequency of the

power theft detection data and the average theft

current. Once the critical grid area is identified, then

MCS decides the hop count (H) for the multi-hop

transmission and the periodicity of the power theft

check, based on the measure of criticality.

Let there are ‘m’ number of MGAs in a

microgrid. The EPTDNA for efficient networking of

power theft data in MCS of the microgrid is as

follows:

1. Phase I:

2. Collect the power theft data from MGA

MGA

….MGA

using the power theft detection

PowerTheftDetectioninMicrogrids

345

algorithm for T

(in the order of days or weeks)

time duration.

3. Phase II:

4. Calculate {F, Avg(i

)}for each MGA.

5. Classify MGAs based on {F, Avg(i

)} using

algorithms such as medoid or k- means.

6. Get three classes namely FMGA, OMGA and

EUMGA.

7. for(class 1 to class 3)

8. Calculate {Avg(F

), Avg(i

)}

9. T

(unit is same as T

) = 1/Avg(F

)

10. Calculate r = Avg(Max(i

con

))/Avg(i

)

11. if(r≥1 )

12. τ

= T

13. Set h

cout

= [(N

tot

/k)-1]

14. else

15. τ

= T

×r

16. Set h

cout

= [N

tot

-1]

17. end if

18. Send T

msg{MCS

, τ

, h

cout

, MGA

time_stamp} to MGAs in three classes.

19. end

In EPTDNA, MCS will collect the power theft

data from all the MGAs under the control of the

MCS using the power theft detection algorithm for

duration. The MCS calculates the frequency of

data on power theft detection(F) of power theft

detection and average power theft current value for

each MGA. On this two-dimensional data, MCS run

any classification algorithm and classify the MGAs

into three classes. The three classes are Frequently

Identified MGA (FMGA), Occasionally Identified

MGA (OMGA) and Extremely Unlikely Identified

MGA (EUMGA). Then for each derived class, the

MCS again computes the average frequency of data

on power theft detection(F

) and the average power

theft current (i

). MCS finds T

using average of

. For finding the ratio ‘r’, the MCS calculates the

average current draw of the maximum power

drawing consumers for MGAs. After computing the

ratio ‘r’, it sets the time duration for power theft

check (τ

) and the hop count (h

cout

) for the power

theft data for the MGAs in each class. N

tot

is the

total number of SDNs in the MGA and the factor ‘k’

depends on the maximum communication range of

the SDNs.

After finding out the τ

and h

cout

for each class,

the MCS will send this information to the MGAs in

each class. Then the MGAs set the new values for τ

and h

cout

and those values persist the next T

duration. If τ

is too large, then random power theft

check will be introduced for the MGAs. The next

EPTDNA Phase II initiation happens in two cases:

1) After T

duration 2) After the power theft

detection in random power theft check. Table 1

shows the description of notations used in this

research work.

Table 1: Description of notations used in this research

paper.

Notations Description

The current flow

measured b

SDN.

kdir

Direction vector of the

current flowing through K

SDN.

dir

Direction vector of the

ower theft current.

Sum of the current

flowing through all the

descendants of K

SDN.

loss

Transmission loss due to

heat.

val

Tolerance limit for the

error in the received current

value at SDN.

Power theft current.

Time duration for power

theft data collection for

EPTDNA-Phase I.

F Frequency of data on

power theft detection for T

duration.

Frequency of data on

power theft detection for T

duration in a class.

Power theft current in a

class.

con

Current draw by the

consumer.

Time duration after which

periodic power theft check

happens.

cout

Hop count of data

transmission for power theft

check.

tot

Total number of SDNs in

a MGA.

Factor that determines the

cout

and depends on the

maximum communication

ran

e of SDNs

6 PERFORMANCE ANALYSIS

AND RESULTS

To show how EPTDNA with PTDA gives better

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performance than PTDA alone, we assume the

microgrid has fourteen MGAs with ‘F’ and average

of ‘i

’ for 30 days as shown in table 2. We have

applied k-means classification algorithm on the 2-D

data in table 2 (Wilkin and Huang, 2007). The

results of the classification algorithm are shown in

figure 4 and table 3. Five MGAs are identified as

FMGA, six MGAs are identified as EUMGA and

three MGAs are identified as OMGA using k-means

algorithm.

Table 2: Microgrid Areas in a microgrid with ‘F’ and

‘Avg(i

)’used for the analysis.

MGA F Av

)

in Amperes

1 15 19

2 20 18

3 17 16

4 18 17

5 2 8

6 1 6

7 4 10

8 3 7

9 13 5

10 16 10

11 12 8

12 5 15

13 8 17

14 6 14

The ‘Avg(F

)’ and ‘Avg(i

)’values are shown in

table 4. From those values, ‘T

’, ‘r’ and ‘τ

’ values

are calculated for the three classes as shown in table

5. The τ

values are derived based on the ‘Avg(F

)’

and ‘r’ values. The Avg(Max(i

con

)) value for the

microgrid is assumed as 15A.

In this case, the hop count (h

cout

) for transmitting

the data for PT check for MGAs inside the classes

EUMGA depends on the factor ‘k’, which again

depends on the communication range of SDNs used

in the microgrid infrastructure. The ‘h

cout

’ for FMGA

and OMGA, depend on the total number of SDNs in

those MGAs.

If in PTDA, the power theft check is done in

every 15minutes for the whole micro-grid, then after

every 15minutes the SDAs and SMs have to transmit

the data through each and every node. Also the sleep

duration for the nodes should be less than

15minutes. By using PTDA with EPTDNA, from the

table 5, it is clear that the sleep duration of the nodes

increases and the message complexity reduces

Figure 4: K-means plot using ‘F’ and ‘Avg(i

)’ from table

Table 3: Microgrid areas (MGAs) in the identified classes.

MGA F Avg(i

)

in Amperes

Class 3 - FMGA

MGA -1 15 19

MGA -2 20 18

MGA -3 17 16

MGA -4 18 17

MGA -10 16 10

Class 2 - EUMGA

MGA -5 2 8

MGA -6 1 6

MGA -7 4 10

MGA -8 3 7

MGA -9 13 5

MGA -11 12 8

Class 1 - OMGA

MGA -12 5 15

MGA -13 8 17

MGA -14 6 14

Table 4: Avg(F

) and Avg(i

) values for the three classes.

Classes Avg(F

) Avg(i

) in

Amperes

Class 3 -

FMGA

17.2 16

Class 2 -

EUMGA

5.83 7.3

Class 1 -

OMGA

6.3 15.33

relative to the PTDA without EPTDNA. As the cost

for communication is directly proportional to the

message complexity, by using EPTDNA along with

PTDA, the cost for communication can be reduced.

Increased sleep duration of the nodes implies the

reduced energy consumption. Thus the effect of

PowerTheftDetectioninMicrogrids

347

Table 5: T

cl,

r and τ

values for the three classes.

Classes T

(in

days)

r τ

n days)

Class 3 -

FMGA

1.74 0.94 1.63

Class 2 -

EUMGA

5.14 2.05 5.14

Class 1 -

OMGA

4.74 0.98 4.63

energy consumption by SDNs or SMs in the un-

sustainability of microgrid can be reduced. In PTDA

with EPTDNA, the example case shows that the hop

count is reduced in MGAs under EUMGA category.

Thus the data latency is reduced in parts of the

micro-grid, if the consumption data is piggy backed

with the power theft detection data. Thus the three

issues of PTDA without EPTDNA mentioned in

section 4 are solved using EPTDNA.

7 CONCLUSIONS

In this paper we have proposed wireless network

based solution for power theft, which is considered

as a bane of power grid in most of the developing

nations. We have proposed power theft detection

algorithm (PTDA) which uses Kirchhoff’s Current

Law (KCL). We have identified three issues of

PTDA when it will be used for micro-grids. To solve

those issues with PTDA, we have proposed another

algorithm called EPTDNA (Efficient Power Theft

Data Networking Algorithm). The performance

analysis and results given in section 6 shows how

EPTDNA solves the issues with PTDA. In future,

we are planning for a real-world deployment of

microgrid infrastructure that enables efficient power

theft detection and localization using EPTDNA

together with PTDA.

ACKNOWLEDGEMENTS

The authors would like to express gratitude for the

immense amount of motivation and research

solutions provided by Sri. Mata Amritanandamayi

Devi, The Chancellor, Amrita University. The

authors would also like to acknowledge Dr. P.

Venkat Rangan for providing valuable suggestions

to improvise this research work.

This work was supported by TATA Consultancy

Services under TCS Research Scholar Program.

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PowerTheftDetectioninMicrogrids

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