Combating fraud and theft in the smart grid

Based on an article by NES for Smart Energy

The problem and its impact

Power theft in the smart grid is the menace of electric utilities from rural cooperatives to large investor-owned and municipal providers.

As early as 1899, the Association of Edison Illuminating Companies addressed the problem of tampering with screws that adjusted meter damping magnets. Over 36 years ago in 1984, the New York Times published an article citing ConEd and the potential of “1% of power customers of stealing services.”

According to estimates, theft and fraud of electricity costs the industry as much as $96 billion every year globally, with as much as $6 billion every year in the United States alone. This not only results in higher prices for paying customers and costly government subsidies but is also a public safety crisis in some countries with dangerous illegal power connections. In some countries, high non-technical losses threaten the financial sustainability of the energy utilities, with double-digit % energy losses through theft and fraud being estimated.

Non-technical losses

The financial losses, also known as non-technical losses, include metering inaccuracy, non-payment, billing and rate class errors, and simple to complex energy theft. Traditionally, the two dominant components of non-technical losses are non-payment and energy theft (fraud in billing and payment, and tampering with meters), and these are still relevant with smart grids. With increased intelligence in the new smart grids, there is now an opportunity for a more “systematic” approach to energy theft, in the form of billing record tampering, resulting from a cyber-attack.

Not only do these losses cost the industry billions of dollars every year, but they also put the public’s safety at risk, especially in countries where illegal power connections are common. Also, non-technical losses cause prices to go up for paying customers and require expensive subsidies from the government to make up for the losses.

Electric utilities used to find non-technical losses through labor-intensive premise inspections and account audits. However, these methods often cost more than the actual value of the losses, and enforcement is always hard. But improvements in smart grid technology give us new ways to fight non-technical losses in a more organized way. Modern Advanced Metering Infrastructure (AMI) solutions, for example, have ways to find both attempted and actual fraud and theft by using more intelligence. AMI also makes it possible to fully automate the process from the meter to the cash register. This reduces the role of people in the system and makes it harder for fraud to happen. But it’s hard to choose the right technology for the AMI because fraudsters, thieves, cybercriminals, and agents from hostile nations are very smart. Only advanced, scalable, and flexible technologies will be able to stop them.

Until recently, there were few effective solutions for these problems. Labour-intensive premise inspections and account auditing often cost more than the actual value of the losses and enforcement is always challenging.

Bringing it up to date

Modern AMI (Advanced Metering Infrastructure) solutions have ways to find both fraud and theft that has been tried and theft that has already happened. At the same time, this increased intelligence makes us more vulnerable to cyberattacks, which are often used to commit fraud or theft. This means that we need to put in place new ways to protect ourselves.

Before we look at how modern AMI helps DSOs, let’s learn more about how energy theft and fraud are done.

Types of energy theft and fraud

Most of the time, energy theft and fraud fall into the following categories:

Direct theft: The thief can just take energy from the DSO without giving the DSO a chance to measure how much energy is being used. This is done by connecting spurs to the main supply and drawing power straight from it. Because of this, it is often possible to see this from the street.

Meter tampering is when the meter is changed in a way that makes it give wrong readings. Again, this is usually written on the meter, but because most meters are in cabinets or buildings, it’s harder to see from the street. With the introduction of smarter meters, hackers have more chances to mess with the meter or the information it stores, which can be hard to spot.

Billing mistakes: The human steps in the meter-to-cash process are places where the information about how much energy was used can be changed. In traditional networks, this can include everyone from the person who reads the meter to the people who do the accounting. Even though humans have a much smaller role in smarter grids, there are more chances for cyberattacks to change usage and account information. Data analysis and audits are the only ways to find out about either case.

Bills that aren’t paid: Some customers just don’t pay their bills, which makes it hard for the DSO to turn off the power.

Who is the electricity thief?

There are many kinds of people who steal electricity:

Consumer: This can happen to a lot of people and is hard to stop. Most of the time, people steal directly or mess with the meters. The only person who gains from the theft is the customer.

Organized crime: This can be anything from a local technician helping local customers steal energy or tamper with meters to organized criminals influencing the human steps in the meter-to-cash process. The criminal group and the person who “hires” them both benefit.

DSO insider: In this case, a person working in the meter-to-cash process is using their power to change information about metering, billing, or accounts. This is often linked to organized crime, but it could also be a job gone wrong.

Nation-state: Interfering with energy supplies is a known way for countries that don’t like each other to fight. This is usually done by cutting off the country’s energy supply, but fraud and theft attacks can be used to cause financial instability in the country’s key infrastructure providers.

Cybercriminal: In a way, a cybercriminal works for all of these other types of thieves. Their job is to look for places where fraud and theft could happen in the smart energy infrastructures that are becoming more common. They do this by looking for weaknesses in IT security technology and process.

Modes of electricity fraud and theft

A summary diagram of how electricity fraud and theft happen on the physical infrastructure is shown below.

A summary diagram of how electricity fraud and theft happen on physical infrastructure. Image source and credit:

Cybercriminals use IT hacking techniques to get into and change the meter, the networks between the meter and the back-end billing/accounting systems, or the back-end systems themselves. One way is for the cybercriminal to change the settings on the meter so that it can’t record usage correctly or to mess with the functions that are meant to stop fraud and theft. Another way is to access the meter data directly, either on the meter, in the network, or at the back-end systems, and enter false consumption values. The second is much more dangerous because cybercriminals can get very good at hiding the signs that they have messed with something.

New means to detect theft and fraud

With traditional, “non-smart” metering systems in the grid, it was hard to find theft and fraud without audits, either in the streets and homes of consumers, in back-end billing/accounting records, or in records of how processes were carried out.

The latest AMI is a big step forward:

  • Meter tampering and supply alarms: The newest generation of smart meters has a wide range of events and alarms that show when fraud and theft at the meter are being planned or are happening. In the most advanced smart meters, this can include events that show someone tried to break into the meter or the network that connects it to the outside world.
  • Complete automation of the meter-to-cash process, which means that most people can be taken out of the process. The main business reason for starting a smart meter project is often to cut down on theft and fraud.
  • Smart meters allow the DSO to limit access to energy and even cut it off if bills aren’t paid. This can be done from a distance. The remote control is convenient and saves money. It also cuts off the power faster, which means less money is lost and the thief doesn’t have to be confronted in person.
  • Analysis of consumption in the low-voltage grid: In high-quality smart grid deployments, it is now possible to measure how energy flows through the low-voltage grid. This lets DSOs find energy leaks, which could be caused by theft.
  • Low-voltage grid topology analysis. Before you can do an analysis, you need to know how the low-voltage grid is set up. Many DSOs have trouble with this because records may be out of date or don’t exist at all. The latest AMI solutions can figure out the topology from the number of communications.
  • Usage statistics: Some illegal uses of energy, like growing drugs, can be found by looking closely at patterns of use and how they relate to other data, like the weather.

These are very useful features, but they need to be paired with back-end systems that can show and make sense of the new information the meter generates and make it easier to take action. In modern AMI deployments, the role of operational tools that provide monitoring and control features needs to be carefully thought out. If this isn’t done, the smart grid’s new insights will be wasted.

Last but not least, what about cybercriminals? The best AMI solutions have strong security measures that make it hard for hackers to get into systems. History shows that “hard” does not mean “impossible.” The newest developments in smart grid security deal with spotting threats and figuring out what to do about them. This means being able to spot activities that point to a possible, actual, or successful attack on smart meters. When put to use, this information can be used to stop cybercriminals from committing fraud or energy theft.

In summary, the mechanisms are now available to combat theft and fraud close to its source with the new generation of Smart Meters and technologies like the VECTO System that provide time-synchronized local and grid-wide monitoring and alerting on low-voltage and medium-voltage ingress and egress points. The selection of the right technology for the AMI is key. Only sophisticated, extendable, and agile technologies will be effective against the ingenuity of the fraudster, the thief, the cyber-criminal and the hostile nation agent.

End of adapted original article by NES for Smart Energy

Introducing VECTO System – Grid Wide Billing validation at the Low-Voltage and Medium Voltage level

Developed in Cape Town, South Africa, VECTO System is an innovative grid management system developed to meet Africa’s steep energy challenges. It is a solution in two parts – a device installed across the network, and a software platform that visualises the data and provides real time notifications when network performance moves out of accepted safety thresholds.

Vecto 3 device

Each VECTO System device is a linux-based edge computer, which process data locally as it enters the device, while simultaneously streaming it onwards a central data store. With a built-in GPS clock electrical parameter and billing data is time synchronised to within ±100ns from absolute time, the full fleet of devices work together in perfect harmony, delivering the full picture of network performance.  

VECTO 3 records and reports on a comprehensive set of RMS, phasor, harmonic, environmental & synchrophasor data, encompassing over 9,000 parameters. Additionally the system can be used as a independent data source for electricity billing forensic auditing and validation.

VECTO System’s data visualisation platform — VECTO Grid OS — reports and visualizes the data for the end user. Accessible on PC and all smart devices, VECTO Grid OS will notify the appropriate team members at the moment anomalies occur on the network.Beyond emergency notifications, VECTO System’s unique capabilities can also:

  • Provide interaction and control down to the mini-substation level, providing engineers and operators with unprecedented visibility and remote management of the entire enterprise.
  • Predict, detect and prevent based on custom campaign parameter configurations.
  • Provide detailed information and insights through an ongoing forensic record, enabling long-term decision making and informed capital investments.

Keen to know more?

VECTO System is set to change the way the power grid is managed. If you’d like to see more of what the system is capable of, speak to us.


AU – Jaiswal, Supriya
AU – Ballal, Makarand S.
PY – 2020
DA – 2020/05/01
TI – Fuzzy Inference Based Electricity Theft Prevention System to Restrict Direct Tapping Over Distribution Line
JO – Journal of Electrical Engineering & Technology
SP – 1095
EP – 1106
VL – 15
IS – 3
AB – Electricity theft is a major concern for power distribution utilities. The increase in non-technical losses give rise to imbalance between electricity supply and demand resulting into overloading of existing distribution network, reduction in reliability and stability of supply and additional tariff posed on genuine consumers. Although, the smart metering systems has resolved meter related power theft problems, however, direct tapping on distribution line remains perpetual issue which should be stringently annihilated. Thus, this paper presents real-time electricity theft detection using energy consumption data of all legal consumers and outgoing distribution transformer energy meter data. In order to prevent the hook-line activity, a fuzzy inference based scheme is implemented in LabVIEW to operate electricity theft prevention system (ETPS). The ETPS develops unsuitable voltage across illegal consumer and hinders normal operation of their appliances. The consumer care unit (CCU) interlocked with ETPS maintains normal supply voltage at legal consumers end. The suitability, flexibility in operation and effectiveness of the proposed ETPS and CCU based theft prevention scheme is experimentally and practically demonstrated as case study under various voltage regulation and energy loss scenarios.
SN – 2093-7423
UR –
DO – 10.1007/s42835-020-00408-7
ID – Jaiswal2020
ER –

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