Electricity theft is smart meter a soln


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Electricity theft is smart meter a soln

  1. 1. Electricity theft - Is smart meter a real solution? Prof C.Balasubramanya 1.0 Introduction 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 billing procedure  Conventional energy meters.  Smart 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.  Technical losses.  Non-Technical/Commercial issues. 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 third parties 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 reasons.
  2. 2. 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 revenue 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 techniques include:  Modern smart meters  Preplanned smart meter installation topology  Analysis of smart meter readings to identify theft profiles  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.
  3. 3. Preplanned Installation Topology of Smart Meters 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 consumer installations. 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.
  4. 4. In the above figure Illegal Load 3 is detected by Branch Sum Meter 1; however, Illegal Load 4 goes undetected. 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 Theft Profiles 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 when:  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. Unpaid Bills 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 tangible object. 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 post-paid service). 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).
  5. 5. 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 central station.  Detection of fraud by analyzing the central database. 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. 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 above. 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 smart meters. 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,
  6. 6. 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. 5.0 Summary 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 events 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 systems. 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, M. R. 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. www.lesco.gov.pk (12-04-2012) 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.”