Detection of Energy Theft in Overhead Low-Voltage Power Grids – The Hook Style Energy Theft in the Smart Grid Era

This paper investigates the possibility of detecting the hook style energy theft in the overhead low-voltage (OV LV) power grids when the smart grid conveniences are available. On the basis of the broadband over power lines (BPL) technology and the proposed method of the detection of the hook style energy theft (HS-DET method), a plethora of different scenarios concerning the hook style energy theft is considered so that the performance of HS-DET method can be assessed. The impact of OV LV BPL topologies, hook characteristics and measurement differences on the performance of HS-DET method is mainly assessed through appropriate metrics, such as derivative metrics of percent error sum (PES). Finally, appropriate contour plots against the hook style energy theft are proposed revealing the efficiency of HS-DET method against any relevant threat in any conditions.Citiation: Lazaropoulos, A. G. (2019). Detection of Energy Theft in Overhead Low-Voltage Power Grids – The Hook Style Energy Theft in the Smart Grid Era. Trends in Renewable Energy, 5(1), 12-46. DOI: 10.17737/tre.2019.5.1.008

Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

As big data, its technologies, and application continue to advance, the Smart Grid (SG) has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology (ICT) and cloud computing. As a result of the complicated architecture of cloud computing, the distinctive working of advanced metering infrastructures (AMI), and the use of sensitive data, it has become challenging to make the SG secure. Faults of the SG are categorized into two main categories, Technical Losses (TLs) and Non-Technical Losses (NTLs). Hardware failure, communication issues, ohmic losses, and energy burnout during transmission and propagation of energy are TLs. NTL’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft, along with tampering with AMI for bill reduction by fraudulent customers. This research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load profile. In our proposed methodology, a hybrid Genetic Algorithm and Support Vector Machine (GA-SVM) model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London, UK, for theft detection. A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%, compared to studies conducted on small and limited datasets

Review of Non-Technical Losses Identification Techniques

Illegally consumption of electric power, termed as non-technical losses for the distribution companies is one of the dominant factors all over the world for many years. Although there are some conventional methods to identify these irregularities, such as physical inspection of meters at the consumer premises etc, but it requires large number of manpower and time; then also it does not seem to be adequate. Now a days there are various methods and algorithms have been developed that are proposed in different research papers, to detect non-technical losses. In this paper these methods are reviewed, their important features are highlighted and also the limitations are identified. Finally, the qualitative comparison of various non-technical losses identification algorithms is presented based on their performance, costs, data handling, quality control and execution times. It can be concluded that the graph-based classifier, Optimum-Path Forest algorithm that have both supervised and unsupervised variants, yields the most accurate result to detect non-technical losses

Smart Decision-Making via Edge Intelligence for Smart Cities

Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and providing these AI applications is non-trivial and will require sufficient computing resources. Traditionally, cloud computing infrastructure have been used to process computationally intensive tasks; however, due to the time-sensitivity of many of these smart city applications, novel computing hardware/technologies are required. The recent advent of edge computing provides a promising computing infrastructure to support the needs of the smart cities of tomorrow. Edge computing pushes compute resources close to end users to provide reduced latency and improved scalability — making it a viable candidate to support smart cities. However, it comes with hardware limitations that are necessary to consider. This thesis explores the use of the edge computing paradigm for smart city applications and how to make efficient, smart decisions related to their available resources. This is done while considering the quality-of-service provided to end users. This work can be seen as four parts. First, this work touches on how to optimally place and serve AI-based applications on edge computing infrastructure to maximize quality-of-service to end users. This is cast as an optimization problem and solved with efficient algorithms that approximate the optimal solution. Second, this work investigates the applicability of compression techniques to reduce offloading costs for AI-based applications in edge computing systems. Finally, this thesis then demonstrate how edge computing can support AI-based solutions for smart city applications, namely, smart energy and smart traffic. These applications are approached using the recent paradigm of federated learning. The contributions of this thesis include the design of novel algorithms and system design strategies for placement and scheduling of AI-based services on edge computing systems, formal formulation for trade-offs between delivered AI model performance and latency, compression for offloading decisions for communication reductions, and evaluation of federated learning-based approaches for smart city applications

Enabling sustainable power distribution networks by using smart grid communications

Smart grid modernization enables integration of computing, information and communications capabilities into the legacy electric power grid system, especially the low voltage distribution networks where various consumers are located. The evolutionary paradigm has initiated worldwide deployment of an enormous number of smart meters as well as renewable energy sources at end-user levels. The future distribution networks as part of advanced metering infrastructure (AMI) will involve decentralized power control operations under associated smart grid communications networks. This dissertation addresses three potential problems anticipated in the future distribution networks of smart grid: 1) local power congestion due to power surpluses produced by PV solar units in a neighborhood that demands disconnection/reconnection mechanisms to alleviate power overflow, 2) power balance associated with renewable energy utilization as well as data traffic across a multi-layered distribution network that requires decentralized designs to facilitate power control as well as communications, and 3) a breach of data integrity attributed to a typical false data injection attack in a smart metering network that calls for a hybrid intrusion detection system to detect anomalous/malicious activities. In the first problem, a model for the disconnection process via smart metering communications between smart meters and the utility control center is proposed. By modeling the power surplus congestion issue as a knapsack problem, greedy solutions for solving such problem are proposed. Simulation results and analysis show that computation time and data traffic under a disconnection stage in the network can be reduced. In the second problem, autonomous distribution networks are designed that take scalability into account by dividing the legacy distribution network into a set of subnetworks. A power-control method is proposed to tackle the power flow and power balance issues. Meanwhile, an overlay multi-tier communications infrastructure for the underlying power network is proposed to analyze the traffic of data information and control messages required for the associated power flow operations. Simulation results and analysis show that utilization of renewable energy production can be improved, and at the same time data traffic reduction under decentralized operations can be achieved as compared to legacy centralized management. In the third problem, an attack model is proposed that aims to minimize the number of compromised meters subject to the equality of an aggregated power load in order to bypass detection under the conventionally radial tree-like distribution network. A hybrid anomaly detection framework is developed, which incorporates the proposed grid sensor placement algorithm with the observability attribute. Simulation results and analysis show that the network observability as well as detection accuracy can be improved by utilizing grid-placed sensors. Conclusively, a number of future works have also been identified to furthering the associated problems and proposed solutions

A Design of Theft Detection Framework for Smart Grid Network

Energy loss and energy theft are two serious problems in modern grid which produce huge waste and cost. The smart grid with its ability to collect information about the behaviors of suppliers and customers is expected to be better equipped than the existing grid to detect loss and theft. The following two questions are the main focus of our works: 1). ``Can we locate the source of theft ?'' 2).``How much energy is stolen?" We deal with two types of theft: tampering with a smart meter and tapping a line. For tampering, we propose a framework based on the measurement of energy, electric current and voltage to make theft detection feasible. In this framework, when measurements (of energy, electric current and voltage) are available everywhere, theft can be easily detected. The interesting case is, if measurements are not everywhere, theft detection is still feasible under some conditions. For different cases of measurement scenarios, we propose different solutions and provide the conditions under which our solutions work. In particular, assuming that the smart grid has a tree structure and has a single source of energy, we show via simulation the following results: 1) With the measurement of electric current at the entry of each user and at the source of energy, we can locate the source of theft if the electric power is stolen in a constant rate and the measurement noise is comparatively small; 2) With the measurement of the energy production and each user's energy consumption plus the measurement of electric current at the entry of each user, we can accurately estimate the resistance of each link as long as the amount of stolen energy is comparatively small; 3) With the measurement of the voltage and electric current at the source of energy and at the entry of each user, we can accurately estimate the resistance of each transmission link if there is no theft. For tapping, we apply clustering algorithms to analyze the anomalies in the usage data of all customers. We propose a hierarchical clustering algorithm which recursively bi-partitions the data along the principle eigenvector and separate the usage data of normal users and abnormal users. Our theft detection framework employs the $\ell_1$ minimization under non-negative constraint, i.e., ${\underset{x \ge 0}{\text{min }}} \| Y-Ax \|_{\ell_1}$. As a theoretical verification of our work, we prove that under some suitable conditions on the matrix A, the $\ell_1$ minimization problem has a unique minimizer and the unique minimizer is equal to the real underlying result

Detecting Energy Theft and Anomalous Power Usage in Smart Meter Data

The success of renewable energy usage is fuelling the power grids most significant transformation seen in decades, from a centrally controlled electricity supply towards an intelligent, decentralized infrastructure. However, as power grid components become more connected, they also become more vulnerable to cyber attacks, fraud, and software failures. Many recent developments focus on cyber-physical security, such as physical tampering detection, as well as traditional information security solutions, such as encryption, which cannot cover the entire challenge of cyber threats, as digital electricity meters can be vulnerable to software flaws and hardware malfunctions. With the digitalization of electricity meters, many previously solved security problems, such as electricity theft, are reintroduced as IT related challenges which require modern detection schemes based on data analysis, machine learning and forecasting. The rapid advancements in statistical methods, akin to machine learning techniques, resulted in a boosted interest towards concepts to model, forecast or extract load information, as provided by a smart meter, and detect tampering early on. Anomaly Detection Systems discovers tampering methods by analysing statistical deviations from a defined normal behaviour and is commonly accepted as an appropriate technique to uncover yet unknown patterns of misuse. This work proposes anomaly detection approaches, using the power measurements, for the early detection of tampered with electricity meters. Algorithms based on time series prediction and probabilistic models with detection rates above 90% were implemented and evaluated using various parameters. The contributions include the assessment of different dimensions of available data, introduction of metrics and aggregation methods to optimize the detection of specific pattern, and examination of sophisticated threads such as mimicking behaviour. The work contributes to the understanding of significant characteristics and normal behaviour of electric load data as well as evidence for tampering and especially energy theft