Research paper

1. Communication Networks and Non-Technical Energy Loss
Control System for Smart Grid Networks
Perumalraja Rengaraju and Shunmugham R. Pandian
Department of Information Technology, Velammal College of
Engineering and Technology, Madurai, India
rpr@vcet.ac.in and srp@vcet.ac.in
Chung-Horng Lung
Department of Systems and Computer Engineering
Carleton University, Ottawa, Ontario, Canada
chlung@sce.carleton.ca
Abstract— In smart grid networks, the traditional electrical
networks are automated using sensors and control algorithms,
where the control information flows in a communication network
to the control center. The control information is delay sensitive
and requires a reliable communication. Hence, it is necessary to
select the best communication technology from the available
candidates to satisfy the high Quality of Service (QoS)
requirement of control information. Among various applications
involved in a smart grid network, providing solution to control
non-technical losses including electrical theft is one of the most
serious problems in developing countries. Therefore, this paper
discusses the suitable communication networks and proposes a
framework to control non-technical losses in energy distribution
systems. The non-technical losses in customer premises like
tampering of electrical devices are conveyed, whereas an
unauthorized theft in overhead lines is computed at the control
center. Then, the control center identifies the electrical theft in a
particular segment of the feeder and tries to identify the exact
location using unmanned aerial vehicle (UAV). Finally, the
control center finds the nearest staff personnel using Global
Positioning Systems (GPS) and conveys power loss and theft
details using General Packet Radio Service (GPRS) network to
control the electrical theft.
Keywords-Smart grids, communication networks, non-technical
losses, electric theft detectionusing UAVs
I. Introduction
In general, electrical power networks have four segments
in the order of electricity generation plants, transmission
systems, distribution systems, and end users. The transmission
and distribution systems include substations for voltage
conversions and transmission/distribution power lines. In the
traditional electrical network design, the focus is mainly on
minimizing investment costs to reduce the cost of energy for
end users. There is no automation in generation to balance the
power requirement as well as in distribution systems to control
and monitor the power usage. However, due to the growth of
population and usage, the power requirement is drastically
increased and the demand is unstable with respect to time and
space. This leads to sudden overloads, voltage sags, and
blackouts in the electric grid. Hence, it is necessary to balance
the generation and consumption of electric power for safer and
reliable operation. For balancing the power requirement and
generation, certain power generating stations are temporarily
connected with the electric grid to meet the excess power
requirement. On the other hand, the power requirement can be
forcefully reduced using load shedding at distribution
substations and controlling the usage of power at customer
side using necessary monitoring systems. Therefore, by adding
these automation functionalities to traditional electrical
networks using data communication results in a smart grid
network.
Smart grid networks deliver electricity more efficiently
and reliably to the customers by automatically re-routing
power, adding power generating sources, load shedding, and
controlling the usage of power at the customer side. In order to
support these functionalities, the smart grid network integrates
advanced sensing technologies, control algorithms,
communication infrastructure, and an actuator/physical
system. The basic building blocks of the smart grid and its
implementation on the electric grid are given in Table I, where
advanced sensing and communications are one of the key
technologies to realize the smart grid.
TABLE I. BUILDING BLOCKS OF SMART GRID [1]
Building Blocks Implementation on Electric Grid
Sensor system
Current transformers (CT), Voltage transformers,
Phasor Measurement Unit (PMU), Sensing relays,
Advanced Metering Infrastructure (AMI), etc.
Communication
infrastructure
Communication between control centers and Home
Area Network (HAN), Distribution Area Network
(DAN), Core Network.
Control
algorithms
Demand response for load balancing, Supervisory
Control and Data Acquisition (SCADA), Volt/Volt-
ampere reactive Optimization (VVO), etc.
Actuator/
Physical system
Distributed generation for load sharing, breakers, on-
load tap changers in transformers, automatic switches,
AMI for load management at HAN, etc.
In a geographical network view, the smart grid
communication network is divided in to access network,
Distribution Area Network (DAN), and core network. The
access network can be further divided in to Home Area
Network (HAN), and Neighborhood Area Network (NAN).
The two main requirements for a communication network are
high QoS and strong security. The high QoS requirement is to
handle both the real-time control information and monitoring
data, whereas the strong security requirement is to handle the
metering and other customer information. In addition to the
QoS and security requirements, it is necessary to analyze the
various components involved in communications and traffic
characteristics for designing the communication network. The
applications involved in smart grid networks and various
communication technologies to meet the QoS and security
requirements are discussed in Section III. Among various
applications involved in the smart grid networks, identification
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2. and providing solution for non-technical losses including
electrical theft is one of the most challenging tasks.
In developing countries like India, both energy demand
and non-technical losses due to electricity theft are increasing
dramatically every year. In India, the consumers are utilizing
approximately 3.4% global energy consumption due to
population and the growth of the energy demand is 3.6% per
annum over the past 30 years. On the other hand, only 55% of
the total energy is billed and 41% is realized [2]. Theft of
electricity is so pervasive in India that 15 to 30 percent of
power is lost to illegal hookups, bill fraud, or nonpayment.
The World Bank estimates that stealing from the grid reduces
India’s gross domestic product by 1.5 percent [3]. As a result,
power cuts due to load shedding are quit common across
Indian cities. Therefore providing the best solution for
controlling electrical thefts and other non-technical losses, and
selecting the best communication path are current
requirements for the smart-grid research.
Very recently, drones have been used to identify the theft
of cooper wiring in Europe [20]. Hence, we are motivated to
analyze the electricity theft in overhead lines using drones. In
our proposed approach, the control center conveys in real-time
the analyzed information from images of transmission line
power thefts to the authorities using GPS and GPRS networks.
The rest of the paper is organized as follows. The existing
works related to communication networks in smart grids and
solutions for non-technical losses are discussed in Section II.
The communication network requirements for different
applications and the technologies available for implementing
the access network and the DAN are studied in Section III.
The various non-technical losses and the proposed solution for
electricity theft detection and feeder losses are explained in
Section IV. Lastly, Section V presents the conclusions and the
scope for future work.
II. RELATED WORKS
For implementing communication networks in a smart
grid, a single communication technology would be inadequate
to cover the entire geographical area and multiple applications.
In general, optical communication is used for the core network
design to support the aggregated QoS-sensitive traffic. On the
other hand, in order to support various applications, such as
Distribution Automation (DA), protection using SCADA (see
Table I), etc., various communication technologies are
considered for access network and DAN networks. The
communication technologies such as Narrow Band - Power
Line Communication (NB-PLC), Broadband over powerlines,
e.g., PLC, (BPL), DSL, ZigBee, Bluetooth, Wi-Fi, cellular,
microwave, WiMAX, LTE, and proprietary mesh networks,
will play a key role in smart grid applications. Some important
works on communication infrastructure for the access
networks and DAN are listed below:
In our previous work [4], various technologies used for
constructing the DAN were analyzed and we concluded that
4G wireless technologies, WiMAX and LTE could be the best
candidates among all available candidates. However, the
spectrum usage and long range Wi-Fi were not considered in
that analysis. In contrast, the throughput analysis of the long
range Wi-Fi network was studied in [5], where long range Wi-
Fi supports 12 km distance for a transmission rate of 6 Mbps.
Parikh, et al., discussed potential smart grid applications using
different wireless technologies: wireless LAN, WiMAX,
ZigBee, 3G/4G cellular, MobileFi, digital microwave and
Bluetooth [6]. For the access and HAN, Vijayakumar, et al.
[7] simulated the environment with 11 smart devices
communicating through a ZigBee technology. The simulation
results showed that, in a 24-hour simulation, the end-to-end
delay in the network ranged between 35 ms and 80 ms, and the
throughput ranged from 90 Kbps to 100 Kbps.
On the other hand, some real-time implementations in Italy
and Sweden use PLC and some of the pilot projects are
studied using DSL connections that are non-wireless standards
[8]. Laverty, et al., considered the standard, International
Electro-technical Commission (IEC) 61850 for substation
automation and NB-PLC for individual Low Voltage (LV)
applications [9]. The detailed study of communication
requirements for different applications and technology
involved in smart grid network were reported in [8-12]. The
opportunities and challenges of wireless networks and possible
applications in smart grid network were discussed in [8].
However, one of the key smart grid applications that require
more research and study for developing countries is the
identification of electrical theft and other non-technical losses.
In other research [13, 14], power line impedance technique
is also used to determine the location of an illegal consumer or
tapping at the feeder. The phase angle and impedance values
of the transmission lines at two different operating frequencies
are measured for calculating the location of the theft.
Impedance of the power line is measured at 50 Hz and is then
compared with impedance measured on the same transmission
line, when a signal of about 2 V at 150 KHz signal is sent. On
the other hand, smart prepaid energy metering systems are
used to control electricity theft at customer locations [15],
where a smart energy meter is installed in every consumer unit
and a server at the service provider side is used to recharge the
PIN number using GSM infrastructure. Similarly, the
electricity theft detection at the customer location is identified
using ZigBee module in [16]. In one of the other previous
works, a GSM module is used to identify the electricity theft
in [17], but the exact location was identified using the
BOUNCE algorithm. Similarly, an electrical power theft
detection system in [18] is used to detect an unauthorized
tapping on distribution lines. In [18], the theft detection is
identified using the difference in power values of the total
transmitted energy and the consumed energy through meter
readings, then a wireless module is used to convey the
information and disconnect the power supply locally.
Very recently, drones have been used to identify the theft
of cooper wiring in Europe [20]. Hence, we are motivated to
analyze the electricity theft in overhead lines using drones.
Once the control center finds the possible location of electrical
theft in feeder line, drones will be used to pinpoint the exact
location of an electrical theft. Finally, the power loss value and
theft details are conveyed to the authorities using GPS and
GPRS network.
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3. III. COMMUNICATION NETWORK DESIGN
The control center in a smart grid is used to take
preventive actions during critical situations, like when the
power consumption exceeds the peak load, etc., and in the
course of normal operations it analyzes the power
consumption and billing operations. It requires a high
performance network to handle the control operations from
substations, such as PMU, CT, VVO, SCADA (see Table I),
etc., and bulk metering data from the customer places. The
Smart Grid Communication Network (SGCN) consists of core
network, DAN (also called as field area network), and access
network. The core network of SGCN inter-connects the
control centers, power generating stations, and DANs. In order
to meet high QoS requirements, the best choice for the core
network communication is an optical network.
On the other hand, the major realization of smart grid
applications can be visualized in the DAN and the access
networks. The intelligent distribution substations in DAN and
AMIs in the access network play a major role for balancing
power generation and power consumption. An intelligent
substation in a smart grid has a range of capabilities to
automate power distribution and local functions of the
substation. Similarly, an AMI has a range of capabilities to
monitor the power consumption and control the electrical
appliances within a home/customer area. A high level end-to-
end communication architecture of the access network and the
DAN is shown in Figure 1. The major applications involved
and the QoS requirements for the HAN, NAN and the DAN,
as shown on top of Figure 1, are described as follows:
Figure 1. Smart grid network architecture -[7]
The HANs are inherently a multi-vendor environment
composed of electrical appliances and devices that need to
communicate with the AMI. The AMI is a system that gathers
data on client consumption and transmits that information
back to the control center on a systematic basis for monitoring
and billing purposes. Classical AMI systems can send the
metering data to the control center for every 15 minutes to
once per hour [4], so that the consumers can be informed of
how much power they are using to control their power
consumption. The recent AMI system supports improved
outage restoration process, voltage monitoring, Critical Peak
Pricing (CPP), Time of Use (TOU) metering, etc. The
applications involved in CPP include load management by
controlling water heaters, air conditioners, Plug in Hybrid
Electrical Vehicles (PHEV), and other heavy load appliances
during peak load conditions. The bandwidth requirements for
HANs are low (1-10 Kbps), but ease-of-configuration, plug-
and-play, and low power consumption factors are essential.
The area covered by the HAN is approximately 1000’s of
square feet. The standards ZigBee (IEEE 802.15.4), Wi-Fi,
and Home plug (PLC) satisfy these requirements.
On the other hand, the NAN network needs to cover over a
few square miles to interconnect hundreds of AMI at the
customer places. The groups of AMIs are connected to the
collector node (usually fixed at distribution poles) for
backhaul connectivity. The collector node relays the traffic
between control centers and AMIs. The communication
payloads involved in NAN include meter reading, demand
response, remote disconnect for load control, local command
messages, etc. The main requirements of the NAN are higher
bandwidths (100–500 Kbps) and two-way communications
capability. The end-to-end latency requirements of the
applications involved in NAN are 1-15 sec. The technologies
meeting these requirements are WiMAX mesh, long range Wi-
Fi, 900 MHz proprietary mesh network, and BPL.
In general, access network mostly handles the aggregated
metering traffic in the uplink and load control messages for
distribution automation in the downlink. On the other hand, a
DAN interconnects access network, mobile work force units
(voice and video support), and distribution substation
networks to the control center through the core network. For
distribution sub-station, it handles the video surveillance
traffic in the uplink, substations automation using SCADA in
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4. both uplink and downlink. The communication requirements
of some major applications in HAN, NAN and DAN are
presented in Table II [6, 12].
TABLE II. SMART GRID APPLICATIONS FOR DAN
Smart Grid Applications Data rate Latency Traffic Type
AMI – Advanced metering
(~100byte/meter/1measure)
~500 kbps/
collector
2 sec. – 15
sec
Periodic, 15-
60 minutes
Demand Response (Pricing
/load (street light) control )
14 – 100
Kbps
500 msec
– 1 minute
Periodic/
Random
Meter data management,
power monitoring
56 – 100
kbps
2 sec
Periodic /
Random
Plug-in Electric Vehicle 0.1 –2 Mbps 2- 5min Random
SCADA, Power monitoring
Substation control Systems
~9 Kbps /
substation
200 msec Periodic
Distribution Automation /
Grid management
9 – 100
Kbps
100 msec
– 2 sec
Random
Distributed energy resource
and storage control
9 – 56 Kbps
20 msec –
15 sec
Random
Video surveillances (UAV)
64 – 128
Kbps
0.5 – 1 sec Continuous
Data, e.g. s/w maintenance ~32 Kbps N/A Occasional
Telephony (Work force) 8 Kbps 200 msec Random
In order to support all kinds of data such as regular
metering data, QoS sensitive data, bulk surveillance data, etc.,
selection of communication technologies is more important for
the smart grid environment. Among the existing technologies,
the wired communication, PLC, and DSL can use the existing
electrical and telephone conductors as transmission media.
PLC systems operate by impressing a modulated carrier signal
on the wiring system. Different types of power line
communications, including NB-PLC, BB-PLC and BPL, use
different frequency bands, depending on the signal
transmission characteristics of the power wiring used. In
general, wired line technologies are considered as a more
reliable communication media. However, PLC/BPL has a few
major drawbacks: (1) PLC/BPL signals cannot readily pass
through transformers whose high inductance makes them act
as low-pass filters that block the high-frequency signals; (2)
The noise filtering is a major problem, particularly when we
want to collect fine-grained information at high cadence; and
(3) it requires repeaters for long distances.
On the other hand, wireless technologies do not require any
separate cable, but the reliability is less than for wired
transmission. Further, data rate support is limited in certain
wireless technologies such as ZigBee, GPRS, EDGE/UMTS,
etc. However, 4G wireless technologies (WiMAX and LTE)
and long-range Wi-Fi are considered as suitable candidates for
handling large data. The available communication standards
for the HAN, NAN and DAN and their supports are given in
Table III [4, 8].
TABLE III. COMMUNICATION TECHNOLOGIES IN SMART GRID
Attributes DSL BPL ZigBee EDGE/ UMTS
WiMAX /
LTE
Long range Wi-Fi
Data rate 25 Mbps 100 Mbps 250 kbps 384 Kbps / 10 Mbps 100 Mbps 150 Mbps
Range ~1 km ~2 miles ~ 5 km ~5 km ~4 km ~4 km
Flexibility Medium Medium Medium Medium High High
Network support Complex Complex Simple Simple Simple Simple
Cost ~50US$ ~1000US$ ~ 20 US$ ~100US$ ~400US$ ~ 100 US$
Advantage and
applications
Reliable, successful in
real-time execution;
NAN, DAN
Reliable, successful
in pilot project ;
HAN, NAN
Successful in
HAN; HAN,
NAN
Successful in pilot
projects; NAN
High data rate
support; NAN,
DAN
High data rate support
with free spectrum;
NAN, DAN
Limitations Complexity for wiring
Harsh, noisy channel
environment
Low data
rate, short
range
Costly spectrum fees
and lack of coverage
Costly
spectrum fees
Interference due to
other wireless on the
same frequency
From the communication technologies analysis, we
recommend ZigBee for HAN, a combination of ZigBee and
long-range Wi-Fi for NANs. On the other hand, DSL would be
the best candidate for DAN (field area) as it is successful in
many real-time implementations.
IV. NON-TECHNICAL LOSSES IN SMART GRID AND THE
PROPOSED SOLUTION
In a power distribution line, losses due to the transmission
of electric power (impedance loss) are considered as
unavoidable technical losses. On the other hand, non-technical
losses occur due to electricity theft and defective/tampered
energy meters. The most common and simplest way of
stealing electricity is by tapping the overhead distribution
feeder. Another major type of electricity theft is at the
consumer's end, either by manipulation of energy meters or
bypassing energy meter altogether. Though there are many
techniques for tampering and manipulating energy meters,
some of the main/common ones include:
• Tampering of pressure coil or CT on secondary side of
the energy meter
• Grounding the neutral wire or the neutral is kept open, so
that the meter assumes that there is no energy flowing to
the customer.
• Hitting the meter to damage rotating coil, etc.
• Interchanging input output connections
• Inserting a film to disturb the rotation of disc.
• For electronic meters, Radio Frequency (RF) devices are
mounted to affect the reading accuracy
• Tampering crystal frequency of integrated circuits in
electronic meters
• Exposing meters to strong magnetic fields to wipe out
the memory.
• A shunt is installed between the incoming and outgoing
meter terminals.
• Resetting the meter reading
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5. Obviously, if the traditional energy meters are replaced by
AMI, certain types of manipulation performed on analog
energy meters are not possible. Pandey. et al. [16] studied
electricity theft in smart meters and proposed a system using
ZigBee to prevent the electric theft but communication to the
control center is not properly studied. Similarly, a few papers
are concentrated on tapping of overhead lines [17, 18].
However, the BOUNCE algorithm which is used to identify
the exact location is not studied in real-time and it is not
suitable for practical implementation because a different
voltage signal at higher frequency is used to study the
impedance behavior of a transmission line. Hence, we are
motivated to find an alternate method for detecting the tapping
of overhead lines and appropriate communication technologies
to control the electrical theft immediately.
Proposed system:
The proposed smart grid architecture to monitor and control
the electrical theft is shown in Figure 2. The sequential
functionalities of the proposed system at the control center are,
1. Theft detection process
2. Identification of specific area over the feeder line
3. Theft analysis using video surveillance by drones
4. Theft alert using GPS and GPRS network
The detailed description of the proposed system is as
follows:
Figure 2. Proposed system to control non-technical loss in Smart Grid
Theft detection: Recent AMIs have the facility to convey the
energy theft to the control center for tampering and any other
malpractices in AMI. Hence, energy theft due to tapping of
overhead lines is more serious than others. For an energy theft
detection in overhead lines, the control center in our proposed
system calculates the difference between total energy
consumed by the total consumers legally ‘()’ and total
energy supplied to the feeder ‘
()’ at an instant time, t.
The control center has the details of theoretical technical loss
value for each feeder. Hence, if the power difference is beyond
the threshold value for considering a calculated technical loss
and measurement error ‘

6. ’, then there must be an energy
theft, i.e. If (
() − ()

7. ) then, there must be an
energy theft.
Instantaneous power,
() supplied to the Feeder X is
expressed as
() = () × () =
cos
( + ) × cos
( + )
where () and ()are instantaneous voltage and current;
and are amplitudes of voltage and current; ω = 2πf is
angular frequency; and are the total load phase angles.
Instantaneous power consumed ()on the Feeder X is
() = () =
!
() × () =
!
#()
!
() is nothing but summation of power consumed by an
individual () consumer or the power reading received from
an individual (#) AMI meter.
In general, the technical loss due to Transmission and
Distribution (TD) is about 4 to 9 percent [17]. However,
TD loss may vary depending on length of the feeder, type of
loads connected, etc. Hence, an approximate ‘pth’ must be
calculated for each individual feeder.
Identification of specific area over the feeder line: Once, the
control center identifies the energy theft, it is necessary to find
a specific location to minimize the time for inspection.
Otherwise, vigilance person or local authority has to inspect
the whole feeder area that will take longer time and difficult to
catch the theft too. In order to identify the specific area, the
whole feeder is divided in to k segments and beginning of each
segment a CT is connected to know the current flow in the
remaining feeder line. The smart current transformer conveys
the reading to the control center through long-range Wi-Fi
network. Therefore, once the energy theft is identified the
control center informs to all AMIs on the feeder to disconnect
the output power temporarily for a minute. Meanwhile, the
control center takes the CT readings. If there is no energy
theft, then the CT’s value is negligible. Otherwise, there must
be significant current readings from a set of CTs. Consider
four CTs connected on a Feeder X where CT1 is connected
just before the tail end, CT2 is bit far away from CT1 and CT3
2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)
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8. is again but far away from CT2 and finally CT4 is connected
just after the feeder starting point. If CT1 reading is negligible,
CT2, CT3 and CT4 readings are significant and similar values
then the energy theft is occurring between CT1 and CT2 area.
Theft analysis using video surveillance by drones: UAVs
were originally invented mainly for strategic applications, the
most likely being in Border Security and Coastguard missions.
However, UAVs are increasingly being used for other
applications such as in firefighting, in the energy sector,
agriculture, environmental monitoring, etc. Recently, UAVs
have been used to inspect power lines [19] and to monitor
cooper wire theft [20], i.e. an antitheft application. Similarly,
in our proposed system drones will be used to analyze the
suspected segment in the feeder line. Once, the control center
identifies the suspected segment, drones are used for
surveillance on that area. The drones may be kept at the
nearest local offices to monitor the surrounding area and
control center issue the commands for positioning and location
of the area. Hence, approximately 30 minutes time is enough
to start the video surveillance of the suspected area.
The captured pictures from the on-board camera in drones
are transmitted to the control center using long-range Wi-Fi
network as shown in Figure 2. In the control center, the
received pictures from drones are analyzed to identify the
tapping on overhead lines. In the analysis process, tapping on
the overhead lines is compared with a legal connection in each
location. The legal electrification in India includes a fuse
carrier after the tapping in an electric pole. If there is any
difference with legal connection, exact location is identified
from the picture (photos) and the control center conveys the
exact location using the corresponding coordinates from the
drone's on-board GPS. Otherwise, the control center will
suggest the suspected area for further inspection.
Theft alert using GPS and GPRS network: Once the energy
theft is identified, the control center immediately finds the
nearest staff persons using vehicular GPS. The energy theft
may be either at AMI (recent AMI notifies the information to
control center) or at the overhead lines (in our proposed
system, control center identifies non-AMI energy theft). As
soon as the appropriate persons are recognized, the control
center conveys the detailed information using GPRS to control
the theft. If the information leads to the exact location of the
theft then vigilance staff will take further action at the earliest.
Otherwise, the part of the feeder segment will be inspected
manually to catch and control the energy theft. .
V. CONCLUSIONS
In this paper, various communication technologies used in
smart grid networks are discussed. From the communication
network analysis, we recommend that ZigBee will be a most
suitable candidate for HAN, while a combination of ZigBee
and long-range Wi-Fi can be used for NANs. On the other
hand, DSL would be the best candidate for DAN (field area)
as it is successful in many real-time implementations. Then,
we proposed a non-technical energy loss control system for
smart grid networks. As recent AMIs have the ability to
inform on tampering and any other malpractices locally,
energy theft due to tapping of overhead lines is more serious.
Hence, our proposed system recognizes the energy theft on a
precise part in a feeder line. Once the precise part is identified,
drones are used to analyze the theft using video surveillance.
Therefore, it is possible to identify the exact location of the
theft. Finally, the control center finds the nearest vigilance or
other staff using GPS and conveys the information using
GPRS network. Thus, our proposed system controls the energy
theft at the earliest and works better than other existing
approaches.
REFERENCES
[1] Garrity T.F, “Getting Smart”, IEEE Power and Energy Magz., 2008.
[2] Energy Sector in India – Wikipedia, the free encyclopedia. Source:
http://en.wikipedia.org/wiki/Electricity_sector_in_India
[3] http://www.csmonitor.com/Commentary/the-monitors-
view/2012/0802/India-blackout-flips-a-switch
[4] Rengaraju P, Lung C-H and Srinivasan A, “On the Communication
Requirements of Smart Grid and Analysis of DAN using WiMAX
Technology”, Proc. of 8th
Int’l Conf. on Wireless Comm. and Mobile
Computing, 2012, pp. 666-670.
[5] Tanaka M, Umehara D and Morikura M, “New Throughput Analysis of
Long-Distance IEEE 802.11 Wireless Communication System for Smart
Grid”, Proc. of Int'l Conf. on IEEE SmartGrid Comm., 2011, pp. 90-95.
[6] Parikh P.P, et al., “Opportunities and Challenges of Wireless
Communication Technologies for Smart Grid Applications”, Proc. of
Power and Energy Society General Meeting, 2010.
[7] Vijayakumar V, et al., “On the Communication Requirements for Smart
Grid” IEEE Canadian Review, 2011.
[8] Gungor V.C, Smart Grid Communications: Research Challenges and
Opportunities Tutorial at CCNC, 2011.
[9] Laverty D.M, et al., “Telecommunications for Smart Grid: Backhaul
solutions for the Distribution Network”, Proc. of Power and Energy
society general meeting, 2010.
[10] Pourmirza Z and Brooke J, “A Realistic ICT Network Design and
Implementation in Neighbourhood Area of the Smart Grid”, Scientific
Researh Journal for Smart Grid and Renewable Energy, Sep. 2013,
pp.436-448.
[11] Wang J, et al., “A survey of communication/networking in Smart Grids”,
Proc. of Intl. Conf. on Information Networking, 2011.
[12] Communications Requirements of Smart Grid Technologies, Dep. of
Energy, United States of America, http://www.doe.gov/
[13] Wijayakulasooriya J.V, et al., “Remotely accessible single phase energy
measuring system,” Proc. 1st
Int’l Conf. on Industrial and Information
Systems, 2006, pp. 304-309.
[14] Pasdar A and Mirzakuchaki S, “A Solution to Remote Detecting of
Illegal Electricity Usage Based on Smart Metering”, 2nd
Int'l workshop
on Soft Computing Applications, 2007, pp. 163-167.
[15] Mohammad N, et al., “A Smart Prepaid Energy Metering System to
Control Electricity Theft”, Proc. of Int'l Conf. on Power, Energy and
Control, 2013, pp. 562-565.
[16] Pandey V, et al., “Wireless Electricity Theft Detection System Using
Zigbee Technology”, Int'l Journal on Recent and Innovation Trends in
Computing and Comm., Vol. 1, March 2013, pp. 364-367.
[17] Paruchuri V and Dubey S, “An Approach to Determine Non-Technical
Energy Losses in India”, Proc. of 14th
Int'l Conf. on Advaned Comm.
Tech., 2012, pp. 111-115
[18] Patil S, et al., “Electrical Power Theft Detection and Wireless Meter
Reading”, Int'l Journal of Innovative Research in Science, Eng. and
Technology, Vol. 2, 2013, pp.1114-1119.
[19] Ming Lu, et al., “Research on Auto-tracking Algorithm for Power Line
Inspection Based on Unmanned Aerial Vehicle” Proc. of Asia-Pacific
Conf. on Power and Energy Engineering, 2012, pp. 1-5.
[20] http://www.itnews.com.au/News/359008,german-telco-sends-in-the-
anti-theft-dna-drones-to-mark-cables.aspx
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