A Smart Device for Power Theft Detection

In Maharashtra state industrialization is on the raising slope, hence demand of electricity continuously blooming. But to meet ever-growing energy demand for industrialization as well for house-hold work brings rise in tariff rates. Raising rates of tariff boost the increasing immoral activities in electrical power sector. With a technical point of view, Power Theft adversely affects the power quality, tariff rates and non-reliability of power supply. So power theft is a non-ignorable crime that is highly prevented, as it directly affected the power quality and economy of the state. Electricity theft is a social evil, so it has to be completely eliminated. Power consumption and losses have to be closely monitored so that the generated power is utilized in a most efficient manner. Now Utility companies have prime responsibilities to prevent the illegal usage of electricity. At these stage traditional methods to stop power theft is totally out dated. New technological development highly needed to solve the problem of illegal usage of electricity without any human control. The implementation of such technology will save large amount of electricity, and there by electricity will be available for more number of consumer then earlier with affordable tariff. This paper introduces the electrical power theft controlling mechanism. With the proliferation of smart grid research, the Advanced Metering Infrastructure (AMI) has become the first ubiquitous and fixed computing platform. AMI keeps tracking on real time power consumption that is highly bring transparency in power sector. Drone operated mechanism for direct hooking theft.

Advanced Metering Infrastructure (AMI) is a key enabler of the Smart Grid. Narrowband Power Line Communication (PLC) technology is currently the preferred choice for many AMI use cases. Different PHY/MAC standards and dedicated routing protocols are emerging. New trustworthy models and simulation tools are needed for their evaluation in large scale deployments. This paper presents a realistic model for the latest generation of OFDM-based narrowband PLC standards. The model has been implemented in a popular network simulator - OPNET. Furthermore, the model and the simulator are validated against a real-world testbed. Finally, we present the calibration process and the scalability of the simulator. The results show that it is possible to simulate large topologies of up to 1000 smart meters under a proactive (RPL) or reactive (LOAD) packet routing.

With the substantial increase in implementation of green technology across all sectors worldwide to overcome energy loss, attributes such as reliability, efficiency, productivity, and security become very crucial for a system. Especially with the integration of IoT and smart features into a system alongside the evolution of Internet, cybersecurity and privacy of an individual component should be taken into important consideration. A smart grid comprises wireless network components exposed to cyber-attacks, smart meters being one the most vulnerable part of an advanced metering infrastructure. An advanced metering infrastructure enables two-way communication for automatic meter readings (AMR) and transmits data through wireless network. To counter cyber-attacks afflicting the network of AMI and functioning of a smart meter, additional intrusion detection methods are implemented into the grid network. The review includes cybersecurity concerns associated with smart meters along with an in-depth insight to some major cyber-attacks and their aftermath.KeywordsCybersecurityCyber-attacks advanced metering infrastructure (AMI)Smart metersDenial-of-service (DoS)False data injection (FDI)Man-in-the-middle (MitM)

The protection of smart meters (SMs) from cyberattacks is of utmost importance because SMs in advanced metering infrastructure (AMI) are physically unprotected and produce a large amount of sensitive data. Due to scalability, the SMs are small-sized and low-cost devices having low computational capabilities. The algorithms that are designed to complete the security requirements of SMs should be lightweight. To address this issue, this paper proposes a lightweight security solution to address the man-in-the-middle attack, data tempering, and blockchain-based data provenance. Received signal strength indicator (RSSI) is used to generate link fingerprints, which are used along with pseudo-random nonce to secure AMI. The proposed algorithm detects the involvement of adversarial node or meter tempering by computing other values along with 0 and 1 as the average of consecutive RSSI and difference between the RSSI of connected static SMs. Pearson correlation coefficient ( $\rho $ ) of 0.9102 is achieved when no adversarial node is present in between the connected SMs having mobility in one or both SMs. Negative or approximately equal to zero values of $\rho $ are computed when the adversary is present in the AMI or any of the SM in the AMI is forged. For blockchain-based data provenance, all the hash values of the packet header are 100% matched with the hash functions present at the data concentrator unit (DCU), which shows no adversary’s involvement in AMI. For cases when the adversary is in the AMI, hash functions show no match with the hash values present at the DCU.

The delivery of energy and water meter data, management and control information on separate networks is expensive and defeats the gains of the Advanced Metering Infrastructure (AMI) Smart Grid (SG). In most cases, energy, gas and water services are offered by the same organizational entity, hence the use of different infrastructure for data, service delivery, control and management is expensive and highly illogical. There is a need for a combined energy and water infrastructure to reap the benefits of the AMI SG. Furthermore, combined metering will result in accurate billing, potential cost savings, and improved resource management. This work therefore develops and investigates a combined energy and water AMI smart metering framework. This is possible through a thorough understanding of the AMI technological standards. The implementation of such a system is not trivial, as it depends on many factors: environmental, geographical, technological, economical, regulatory and the existing legacy infrastructure. Optimal technological implementation choices are developed towards an integrated AMI infrastructure. An experimental test bed is developed for delivering energy and water metering data to the utility. The optimal placement results favor the system of separating energy and water actuators at the home area network of the SG while using an integrated communication system. Such a system is feasible, given the different evolution of electricity and water meters and their placement at the home area network, and enables water metering to benefit from the more advanced electrical metering infrastructure.

Technical and governance considerations for advanced metering infrastructure/smart meters: Technology, security, uncertainty, costs, benefits, and risks

Internet of Things connects people and things at any time and in any place. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most significant components in smart grid which collects data from smart meters and sends the collected data to the utility center to be analyzed and stored. To implement two-way communication between smart grid components, machine-to-machine communications has a great potential. In this paper, we propose a machine-to-machine architecture for advanced metering infrastructure in Internet of Things-based smart grid and focus on finding the optimum number of collectors based on two metrics: collectors' cost and packet delay. We model advanced metering infrastructure as a queuing system and derive an expression for packet delay. We also propose a newperformance metric, the product of cost and delay (CD), so that by minimizing it, we can find the optimum number of collectors. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between cost and delay.

AMI (Advanced Metering Infrastructure) is reflected in two-way metering, two-way interoperability on an advanced measurement system. The access network of AMI connecting power utilities to customer's home area network (HAN) promotes the smart metering automation. In such a power line carrier communications medium, the use of OFDM modulation technique is well with the power line carrier communication channel, to effectively resist the power line channel noise, attenuation and time- varying characteristics of the impact. This paper introduces the concept of power line communication, the basic features and principles of the Orthogonal Frequency Division Multiplexing technology (OFDM), focused on analysis of the advantages of OFDM technology used in power line communications.

A review of non-technical loss attack models and detection methods in the smart grid

As smart meter technology advances, the advanced metering infrastructure (AMI) enables real-time monitoring of electricity usage at 15-minute intervals. Since prior studies mainly focused on industrial infrastructures, anomaly detection in residential electricity usage remains underexplored. In this paper, we propose a gated recurrent unit (GRU)-based deep learning model to detect resident changes in residential households using the electricity metering data through AMI. Monthly consumption patterns and energy consumption are predicted using actual electric energy metering data collected from 846 apartment households in Seoul, Korea. To detect the resident changes, combined losses are computed by integrating the consumption pattern and consumption energy prediction errors. The experimental results show an accuracy of 82.55%, specificity of 100%, and recall of 65.11% when allowing a +-1-month error margin. The proposed deep leaning model demonstrates the practical feasibility of detecting resident changes in residential households and is expected to contribute to the development of smart grid services.

Internet of Things connects anythings at any time in any places. Smart grid is one of the promising applications of Internet of Things. Advanced metering infrastructure is one of the most important components of smart grid which aggregates data from smart meters and sends the collected data to the utility center to be analyzed and stored. In this paper, we have proposed an aggregation-based indirect architecture for advanced metering infrastructure architecture in smart grid Internet of Things and focused on finding the optimum number of regional aggregators in its neighborhood area network (NAN) based on three metrics: regional aggregator (RA) power consumption, cost of RAs and wide-area aggregator, and NAN lifetime. We have developed a power consumption model for each RA and define NAN lifetime based on that. We also defined a new performance metric, the product of power consumption and cost (PC), so that by minimizing it, we can obtain the optimum number of RAs. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between NAN lifetime and RAs' cost.

While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

Narrowband (NB) powerline communication (PLC) is extensively adopted by utilities for the communication in advanced metering infrastructure (AMI) systems. PLC technology needs to overcome channel disturbances present in certain grid segments. This study analyzes improvement proposals of the physical layer of the main narrowband PLC technologies approved by international communication organizations that are currently deployed in Europe: Powerline Intelligent Metering Evolution (PRIME) 1.3.6, PRIME 1.4, and G3-PLC, in order to improve PLC performance under channel disturbances. This thorough study is based on simulations carried out by an innovative ad hoc Virtual PLC Lab, developed by the authors, applied in replicable, fully-automated, and cost reduced test scenarios. The analysis is performed by applying standardized test methods and metrics, and by evaluating the influence of a set of representative channel disturbances defined by the European Telecommunications Standards Institute (ETSI) and selected noises generated by distributed energy resources (DER) in normal operation. PLC performance improvements in terms of equalizer curve fitting, error correction codes, and noisy subcarrier suppression mechanisms are presented. The performance gain due to each physical improvement proposal is accurately measured and compared under the same conditions in a replicable and automated test environment in order to evaluate the use of the proposals in the evolution of future PLC technologies.

The demand for enhanced energy management systems is being driven by the desire for sustainable development and the rapid evolution of energy consumption patterns. This study examines the potential for advanced metering infrastructure (AMI) and smart meters to enhance the efficacy of energy transmission and consumption. Smart meters enable customers to monitor their energy consumption in real time, thereby increasing their awareness of their actions and motivating them to adopt more environmentally favorable behaviors. These technologies facilitate two-way communication between utilities and end consumers when integrated with AMI. These features include demand response, automatic invoicing, dynamic pricing, and power outage monitoring. The integration of data analytics and the Internet of Things by AMI enhances the environmental sustainability, operational efficiency, and reliability of the grid. This paper investigates the design, advantages, disadvantages, and potential future improvements of smart energy systems that are powered by AMI and smart meters. We are of the opinion that these technologies will be beneficial in the creation of an intelligent and resilient energy system

Advanced Metering Infrastructure (AMI) is the foundation of the Smart Grid system. One of the most important parts of AMI is smart meter communication. Power Line Communication (PLC) technology utilizes the existing power line network for communication media between smart meters and Data Collector Units (DCU). One of the biggest challenges of communication using PLC is the noise level due to residential loads. In this study, laboratory test set up to measure noise caused by PV rooftop and residential loads are observed. Each PLC frequency band was measured and observed to determine the suitable frequency for PLC communication which was the low noise level. Some experiments in the field were conducted to determine the performance of the PLC. Laboratory test measurement result shows the FCC band (150-490 kHz) was outside the range of PV rooftop and residential load noise. On the other hand, the Cenelec A (35-90 kHz) frequency range was in the range of noise signal. Therefore, NB-PLC G3 using FCC frequency band was proposed to achieve robust data communication and lower noise level. G3 PLC FCC band was tested in the real field residential area, the experimental results showed 97.62% successful rate within one-week data collection.