SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez nos Conditions d’utilisation et notre Politique de confidentialité.
SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez notre Politique de confidentialité et nos Conditions d’utilisation pour en savoir plus.
Electricity theft - Is smart meter a real solution?
Electricity generated is synchronized on a single bus
bar of the grid for transmission. Before utilization of
electricity, it passes through several phases. After
generation, it is stepped up, passed from switch
yard for transmission through power lines. After
transmission it is distributed for utilization to the
customers. This energy needs to be billed as well.
Usually two types of devices are mainly used for
Conventional energy meters.
The one basic reason that whole world is shifting
from analog meters to digital devices is because of
various options that are not possible in the
conventional meters. Analog electromechanical
meters are being substituted by smart meters.
Digital devices provide better security and
controlling options. The better detection and
controlling of losses is one of the reasons for
substitution of smart meters. So the reason for this
Substitution is mainly to minimize losses in electrical
systems. There are mainly two types of losses.
In developing countries electricity theft is a common
practice especially in remote areas, as they do not
pay utility bills. In this paper related work and
motivation is explained & losses are discussed which
are caused due to electricity theft. Ways of
communication to send data from end user to the
grid, Causes and effects of electricity theft. Further it
focuses on issues related to theft are also broached
.The biggest culprits are large residential,
commercial, and industrial consumers who avoid
paying their fair share of electricity, often by colluding
with meter readers, current or ex-utility employees, or
The technology of the smart meter strategy is that
it eliminates contact between the customer and a
utility employee, which is often a factor in
collusion. In many cases, high-use customers are
targeted first. Customers using a lot of energy also
generally will stop stealing it once they realize the
utility has the tools to detect and record the theft.
Some experts also have raised concerns about
the potential for smart meters to be hacked into in
efforts to shut them off, steal data or disrupt power
supplies. The devices are fairly tamper proof, if
meddled with, they send a distress signal that
notifies the utility on a nearly real-time basis.
Such a system helps utilities improve the
performance of the network and it "helps keep
employees honest, because they know the energy
usage overall is being monitored, Global energy
crisis is increasing every moment. Everyone has
the attention towards more and more energy
production and also trying to save it. The main
issue which deals with this paper whether these
losses are technical or non-technical. Technical
losses can be calculated easily, where as non-
technical losses can be evaluated if technical
losses are known. Theft of electricity produces
non-technical losses. To reduce or control theft
one can save his economic resources. Smart
meter can be the best option to minimize electricity
theft, because of its high security, best efficiency,
and excellent resistance towards many of theft
ideas in electromechanical meters. So in this
paper focus is on theft issues
In this paper it is described the capabilities of AMI
to reduce revenue losses due to non-technical
2.0 Electricity theft
Revenue losses caused by electricity theft affect the
quality of supply, the electrical load on the
generating station, and the tariff imposed on usage
by genuine customers.
This paper discusses various new methods offered
by the novel AMI to detect, locate, quantify, and
control incidences of theft.
Quantifying the revenues lost due to electricity theft
results in astonishing figures. The percentage power
stolen ranges from 5% to 40%. If we translate this
loss into financial figures, we can understand the
huge significance of hundreds of millions of lost
2.1 Quantifying Revenue Loss
Quantifying the revenues lost due to electricity theft
results in astonishing figures. The percentage power
stolen ranges from 5% to 40%. If we translate this
loss into dollars, we can understand the huge
significance of hundreds of millions of lost revenue
The table below summarizes recent statistics of
revenue loss due to theft
Electricity theft leads to a series of additional
losses, including damage to grid infrastructure and
reduction of grid reliability. The free power
consumption of thieves results in higher
uncontrollable consumption at peak times, and
therefore higher overall electricity costs to the
utility. In addition, the pirate connection to the grid
by electricity thieves causes severe safety
hazards, both to the thieves and to the general
public. The costs of the above are difficult to
estimate but undoubtedly increase the overall
damages of electricity theft
3.0 Method of Preventing Theft
The most common form of theft is tapping electricity
directly from the distribution feeder and tampering
with the energy meter
AMI presents a variety of methods that detect,
locate, quantify, and control electricity theft. The
Modern smart meters
Preplanned smart meter installation topology
Analysis of smart meter readings to identify
A managed prepaid billing service
Smart Meter-based Techniques
3.1 Theft Detection Features of Smart Meters
Smart meters contain dedicated hardware and
software that detect electricity theft of various
types. The following theft methods are detected
and reported in real time:
Meter covers open alarm – immediately
reports on the opening of the smart meter.
Reverse current alarm – detects rewiring of
the meter causing the current to flow in the
reverse direction (may cause the meter to
count back if not taken care of).
Phase unbalanced alarm – detects
tampering with the electricity wires.
Smart meters can be controlled remotely to
disconnect and reconnect the power supply to the
user, so that reaction to a theft alert can be
immediate if needed.
The meters transmit alarms to the management
server via the concentrator and the
communications system, so the area manager
gets fraud alarms in real time.
Preplanned Installation Topology of Smart
Illegal consumption of electricity can be discovered
by using a remote check meter that detects and
measures the quantity of electricity lost. Measuring
consumer data at regular intervals may initiate
sending vigilance officials to inspect illegal
Using the existing smart meters as a building block,
we can build an AMI topology that implements two
main methods of detecting power theft:
Detecting unmetered consumption
Detecting excessive load
3.2 Detecting Unmetered Consumption
The unmetered consumption detection method
comprises installing a utility AMI with a tree of
meters so that a meter closer to the root
measures the power consumed by the loads below
it. By placing a “sum meter” at a node of the
residential power grid and additional meters below it
that measure the consumption of branches or
specific loads in the tree below, the system
performs a comparison between the sum meter and
the branch/load meters sum. If the sum meter
measurement is greater than the sum of the
downstream meters measurements, it is a strong
indication of an illegal load that consumes power.
The following figure demonstrates this method:
Sum Meter 2 measures consumption that is
greater than the sum of the two branch meters in
the previous diagram.
We can also see that Branch Meter detects the
excess consumption of Illegal Load 2 by comparing
its measurements to the measurements of the three
load meters below.
3.3 Detecting Excessive Load
The second theft detection method is used in
case it is not possible to install a meter for each
load (e.g., lighting grid). In this case the thief can
hook onto the utility grid and not be detected by the
first method. To resolve this case, we use the
inherent capability of smart meters to indicate power
level in excess of preprogrammed value.
In the above figure Illegal Load 3 is detected by
Branch Sum Meter 1; however, Illegal Load 4
To detect Illegal Load 4, we program the
maximum allowed consumption of the three loads
under Branch Meter 11. Once Illegal Load 4 is
active, Branch meter 11 measures the excess
power and indicates a problem.
Using both methods for detecting power theft, an
alert is sent to the utility that can then check the
specific section of the grid and search for the
reason for the alarm.
Analyzing Smart Meter Readings to Identify
Profiling of electricity usage is another important
method to detect electricity theft. The
management server can build a profile of
each meter that contains the consumption
statistics of a specific meter hour by hour.
The profile becomes more and more accurate
over time as additional data is accumulated.
When a meter’s consumption varies above or
below the profile, an “out of profile” notification
appears. The profile tool is very useful in situations
Consumption is lower than usual for a long
time. This may indicate that the consumer
is stealing energy.
Consumption is higher than usual for a
long time. This may indicate that
somebody is stealing from the consumer.
The Management Server produces various reports
that help the utility make better decisions
regarding meter management and maintenance,
as well as thefts and frauds. The reports present
the overall status of the entire area, district, or
utility, and help to analyze the lower levels and
pinpoint frauds and thefts in specific meters.
By analyzing the hourly profile, the area
manager can assess whether a specific meter’s
behavior is suspicious or represents normal
changes in the consumer’s habits.
The Managed Prepaid Billing Service
The phenomenon of unpaid bills is another
major reason for power utilities’ revenue loss.
While current prepaid billing meters use some kind
of physical token (coin, scratch card, etc.), smart
meters offer a “virtualization” capability so that
users can “purchase electricity” without using a
By deploying a friendly, smart, meter-based
prepaid billing service, customers become
accustomed to paying for electricity when they
need it, relieving the utility of having to make the
consumer pay for electricity already used (as in the
Several studies have already demonstrated that
prepaid billing results in considerable energy
conservation by the user (in the USA, a 4-12
percent saving has been measured).
For AMI, prepaid billing is just an additional
service. AMI runs on specific SW that is remotely
controlled to allow electricity usage from a control
center. There is no need for an additional metering
infrastructure and expensive meters (capable of, for
example, receiving coins or smart card numbers).
Since the prepaid service is controlled remotely, it
may have features that allow customers to request
grace periods in case of emergency, thus reducing
the “social” problems of prepaid billing.
4.0 Electricity Theft System
A holistic solution and tools that allow utilities to
fight fraud on three levels. AMI performs:
Continuous regular measurements regardless
of tampering attempts.
Reporting fraud alarms in real time to the
Detection of fraud by analyzing the central
AMI contains fraud-detection facilities in each of its
components: end units, concentrators, and
management server. These facilities combined
provide a powerful and smart integrated system.
The fast response to tampering attempts, along with
the advanced ability to immediately and remotely
disconnect offending consumers, eventually
minimizes the number of offenders.
The Electricity Theft Detection System is part of
solution for AMI. This is depicted in the following
Figure 4: AMI Solution
A typical smart metering project has a 15-20 year
lifespan; therefore, any solution should be
designed modularly, avoiding overall system
changes when a particular part or product
becomes obsolete. In addition, the proposed
solution enables the inclusion of operations
services over a unified and consolidated
communications network, as depicted in the figure
Private ownership of network resources is a
significant advantage, for example:
Network resources may not be guaranteed by
public networks when congestion occurs or in
times of natural disaster.
Public network operators cater to the mass
market, yet their decisions also have direct
effects on power utilities using their networks.
For example, power utilities depending on
public GPRS services for remote monitoring in
a particular country were inconvenienced when
this technology was phased out nationwide in
favor of 3G.
Each concentrator supports up to 2000 electricity
Communications enables real time readings in 2-4
seconds from any smart meter up to 2 km away in
any world location.
smart meters include PLC and RF modules. The
RF is used to correspond with all other smart
meters to measure, control, store, and transmit
information. The electricity meter also
communicates with the in-house display unit to
advise the customer of his consumption and
control of appliances like a water heating system.
The system is able to integrate third party meters
into the system via M-Bus protocol by using a
RS485 to PLC module.
The meter saves the customer contract details and
enables operation according to this profile.
All meters are multi tariff and include electronic
relay for remote shutdown. They store three
months of daily consumption records. The meter
performs additional tasks which enable the system
to offer extra value to the customers. The meters
are built to operate in harsh environments of
temperature and humidity. All meters contain anti-
tamper alerts, such as meter cover open, bypass,
or phase reversal. The operator can configure the
meters remotely to act as credit or prepayment.
The concentrator is typically installed near the
transformer station on the low voltage side of the
MV/LV power transformer. The concentrator
manages the meters via PLC or RF.
The AMI system shall be designed and
implemented with security in mind. Applying third
party security solutions as an overlay is not as
effective, that is, security should be built in and not
bolted on. To be successful, vendor and utilities
alike must possess not only security,
communications, and networking expertise but also
detailed expertise and working knowledge of the
AMI components to allow them to successfully
integrate the secure AMI system solution.
Additionally, an AMI system can reap significant
benefits from deploying monitoring sensors to
detect theft of electricity. The meters are designed
and implemented with the secure attributes
described herein, providing a secure AMI offering to
meet these demanding requirements,
It is concluded that smart meters are not the only
solution to arrest pilferage but minimizes such
This paper discussed a novel solution& offers a
systematic solution to fighting electricity theft from
power utilities. The approach is holistic and
includes real-time detection by the meters and
collection of data and profiling by a management
system. The system produces alarms for the
personnel dedicated to fighting electricity fraud.
The solution also offers an end-to-end solution
with AMI, communications, and management
A Proof of Concept is offered to define the specific
system’s setup and to allow the utility to operate
and understand the system’s benefits
References: C. J. Bandim, J. E. R. Alves Jr., A. V. Pinto Jr, F. C. Souza,
B. Loureiro, C. A.Mangalhaes and F. Galvez-Durand. “Identification of
energy theft and tampered meters using a central observer meter: A
mathematical approach” IEEE 2003.
J. Nagi, A. M. Mohammad, K. S. Yap, S. K. Tiog, S. K. Ahmed. “Non-
Technical Loss for Detection of Electricity Theft using Support Vector
Machines” 2nd IEEE international conference on power and energy.
C. C. O Ramos, A. N. Souza, J. P. Papa, A. X. Falcao. “Fast Non-
Technical Lasses Identification through Optimum-Path Forest.”