Non-technical Losses Research Articles

Fraud detection in low-voltage electricity consumers using socio-economic indicators and billing profile in smart grids

The Brazilian Association of Energy Distribution Utilities estimates that non-technical losses represent more than 5.5% of the total energy distributed, most coming from fraud and theft. To try to mitigate those losses, the distribution utilities send field crews for the inspection of possible fraudster clients. However, the procedure is expensive and gives no financial return to the utility if it is not focused on areas with high fraud probability. On those locations, there is a correlation between losses and socio-economic indices. Thus, this work proposes a model able to select clients with high fraud probability, which should be visited by the field crews. The smart grid structure, energy consumption data, clients' registration data and socio-economic indices from the 2010 Brazilian Census are used by the model.

Identification of the characteristics incident to the detection of non-technical losses for two Colombian energy companies

The study of non-technical losses affecting energy trading companies has guided the researchers’ perspective on different techniques and tools that allow them to detect, and why not, to forecast such losses. In the search for a solution to the problem, the different researchers rely on variables that, in many cases, the same marketing companies, from their practical experience, have been considered as incidents in the identification of the problem. However, most of the studies carried out do not support their solutions with the fact that each trading company retains particular data in which both, technical and socio-economic characteristics recorded, are not necessarily shared in their databases. In this work, we follow up on some of the characteristics registered by two Colombian energy trading companies, which serve two different regions of the country in terms of topography and idiosyncrasy. In particular, attention is focused on two characteristics measured in both companies, which by their nature, will always be on the data of any energy trading company: Consumption in kWh, and the period, measured in months. For this purpose, Benford curves analysis, MultiDimensional Scaling (MDS), and hierarchical cluster will be implemented. Finally, it will be studied if the incidence of the variables visualized in the studies presented is reflected in the decision tree model.

Overview, issues and prevention of energy theft in smart grids and virtual power plants in Indian context

This paper is an excerpt and a comprehensive research on Non-Technical losses during T&D. In developing countries, the utility companies are finding it difficult to address losses due to theft, meter tampering and allied problems which affect the quality of supply, increase on generation load and has a significant economic impact on the functioning of the utility company and its genuine consumers in the form of an excessive tariff. As the single largest contributor to T&D losses, various aspects and methods used for theft and detection mechanisms are discussed. This article also highlights the components of AMI and how a simple method of two smart meters equipped with harmonic generators could be used in conjunction to detect theft.

Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

The advent and progressive deployment of the so-called Smart Grid has unleashed a profitable portfolio of new possibilities for an efficient management of the low-voltage distribution network supported by the introduction of information and communication technologies to exploit its digitalization. Among all such possibilities this work focuses on the detection of anomalous energy consumption traces: disregarding whether they are due to malfunctioning metering equipment or fraudulent purposes, strong efforts are invested by utilities to detect such outlying events and address them to optimize the power distribution and avoid significant income costs. In this context this manuscript introduce a novel algorithmic approach for the identification of consumption outliers in Smart Grids that relies on concepts from probabilistic data mining and time series analysis. A key ingredient of the proposed technique is its ability to accommodate time irregularities – shifts and warps – in the consumption habits of the user by concentrating on the shape of the consumption rather than on its temporal properties. Simulation results over real data from a Spanish utility are presented and discussed, from where it is concluded that the proposed approach excels at detecting different outlier cases emulated on the aforementioned consumption traces.

Metodología para el análisis de costos y beneficios de proyectos Smart Grid y su aplicación en un piloto de medición inteligente de energía (AMI)

The aim of this paper is to present a methodological approach to obtain the cost-benefit impact at environmental and socioeconomic level, related to the integration of Advanced Measurement Infrastructure into utilities and their residential customers. The methodology used to quantify the costs and benefits of implementing such technology was established by the European Union and adjusted to the local context. It is worth highlighting that this methodology can be used to evaluate any kind of Smart Grid project. The socioeconomic benefits estimated in this research were related to job creation, energy consumption decrease, reduction of non-technical losses, and the reduction of CO2 emissions. An economic analysis using the net present value is presented here for assessing the impact caused by smart metering implementation.

Slowly but Surely: Smart Grids Are Just Around the Corner [Guest Editorial

The articles in this special section focus on the development and applications supported by new smart grid technologies. The term smart grid means different things to different people, and quite rightly so. Our relationship with electricity varies significantly around the world. Many countries are trying to decarbonize their electricity systems; for them, a smart grid should be able to integrate large volumes of renewables. In other parts of the globe, the main concern is reducing the number of outages during common and uncommon events (such as natural disasters). For these countries, a smart grid is one that uses innovative solutions to increase reliability and also to make the grid more resilient. We also have countries that simply want to use electricity more efficiently; hence, a smart grid should help, for instance, in reducing technical and nontechnical losses. On the other side of the spectrum, we cannot forget that around a billion people in the world still do not have access to electricity. For those countries, cost-effectiveness and remoteness are major challenges, and a smart grid might be able to bring about innovative yet inexpensive community-based solutions. It is because of this diversity of contexts and objectives that different implementations of smart grid concepts have emerged in the last few years—and continue to emerge.

Detection of energy theft and defective smart meters in smart grids using linear regression

The utility providers are estimated to lose billions of dollars annually due to energy theft. Although the implementation of smart grids offers technical and social advantages, the smart meters deployed in smart grids are susceptible to more attacks and network intrusions by energy thieves as compared to conventional mechanical meters. To mitigate non-technical losses due to electricity thefts and inaccurate smart meters readings, utility providers are leveraging on the energy consumption data collected from the advanced metering infrastructure implemented in smart grids to identify possible defective smart meters and abnormal consumers’ consumption patterns. In this paper, we design two linear regression-based algorithms to study consumers’ energy utilization behavior and evaluate their anomaly coefficients so as to combat energy theft caused by meter tampering and detect defective smart meters. Categorical variables and detection coefficients are also introduced in the model to identify the periods and locations of energy frauds as well as faulty smart meters. Simulations are conducted and the results show that the proposed algorithms can successfully detect all the fraudulent consumers and discover faulty smart meters in a neighborhood area network.

The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.

Evaluation of Customer Behaviour Irregularities in Cameroon Electricity Network using Support Vector Machine

Non-Technical Losses (NTLs) in the Cameroonians electricity network are approximately 30 to 40% of production and are estimated at several billion CFA francs per year for National Electricity Company (ENEO); Hence the importance of finding effective solutions to fight against these losses. The purpose of this work was to develop a tool for the fraud detection for Cameroon National Electricity Company (ENEO) using support vector machines which consisted in data preprocessing base on the load profile, development of a model for classification, parameter optimization and detection of customers irregularities and prediction.

NFD: Non-technical loss fraud detection in Smart Grid

Utility companies consistently suffer from the harassing of Non-Technical Loss (NTL) frauds globally. In the traditional power grid, electricity theft is the main form of NTL frauds. In Smart Grid, smart meters thwart electricity theft in some ways but cause more problems, e.g., intrusions, hacking, and malicious manipulation. Various detectors have been proposed to detect NTL frauds, but they either rely on user behavior analysis which requires a large amount of historical data or needs a lot of extra devices which are expensive. In this paper, a detector named NFD (NTL Fraud Detection), is proposed to detect NTL frauds with only a small amount of data and one additional device. NFD is based on Lagrange polynomial interpolation to model an adversary's behaviors, and detect a tampered meter by comparing the difference between the results. Different from existing detectors, our detector knows adversaries better than adversaries themselves. By building mathematical models of these adversaries, we can predict their behaviors which they may not be aware of by themselves. NFD makes it practical to detect NTL frauds both online and offline in Smart Grid. NFD can facilitate real-world applications before Smart Grid is fully deployed since it can serve the traditional power grid and Smart Grid simultaneously. Experimental results show the effectiveness of NFD. It can detect multiple tampered meters and multiple adversaries, as well as a single tampered meter and a single adversary. We also study how to tune the parameters used in NFD to further guide its practical usage.

A novel detector to detect colluded non-technical loss frauds in smart grid

A Non-Technical Loss (NTL) fraud occurs when a fraudster tampers with a smart meter so that the meter registers less electricity consumption than the actual consumed amount, and therefore the utility becomes the victim who suffers the corresponding economic loss. In the literature, many detection schemes have been proposed to detect NTL frauds. However, some NTL frauds are far more complicated than what the existing schemes expect. We recently discovered a new potential type of frauds, a variant of NTL frauds, called Colluded Non-Technical Loss (CNTL) frauds in the Smart Grid. In a CNTL fraud, multiple fraudsters can co-exist or collaborate to commit the fraud. Existing detection schemes cannot detect CNTL frauds since these methods do not consider the co-existing or collaborating fraudsters, and therefore cannot distinguish one from many fraudsters. In this paper, we propose a CNTL fraud detector to detect CNTL frauds. The proposed method can quickly detect a tampered meter based on recursive least squares. After identifying the tampered meter, the proposed scheme can detect different fraudsters using mathematical models. Our experiments show that our method is effective in detecting CNTL frauds.

Allocation of Power-Quality Monitors Using the P-Median to Identify Nontechnical Losses

This study sets out to develop a procedure that enables monitors to be allocated that are able to estimate values of voltage and current of a circuit. Taking the importance of the load as a starting point, this paper puts forward a model for allocating power-quality monitors in distribution systems based on the P-median model. As a first step, by using an improved model that already exists, the fewest number of monitors which guarantee the observability of the system is obtained. Then, the modified P-median model is applied which incorporates a constraint in order to allocate monitors in accordance with the importance of the load, which is considered as the volume of the load to tackle nontechnical losses. A model such as this is required especially when dealing with nontechnical losses, which can be one of the main causes of undervoltage, thereby affecting the quality of the service. The proposed model was applied in a real distribution network considering the importance of the load throughout the distribution system.

Estimation of Electricity Network Loss Targets in Sri Lanka

This study estimated loss targets for the transmission licensee and the five distribution licensees of Sri Lanka over the period 2016 – 2020. Both technical losses and the nontechnical losses at each component of the network were estimated largely using the data received from all the licensees, and the targets were set mainly considering the reduction of non-technical losses in the system.

The Effect of the Types of Network Topologies on Nontechnical Losses in Secondary Electricity Distribution Systems

The Effect of the Types of Network Topologies on Nontechnical Losses in Secondary Electricity Distribution Systems

Analysis of Non-Technical Losses in Distributed Transformer System

Analysis of Non-Technical Losses in Distributed Transformer System

The low cost of quality improvements in the electricity distribution sector of Brazil

We analyze the impact of introducing output-based incentives in the price-cap regulatory regime of the Brazilian electricity distribution sector. We focus on the trade-off between operating costs and quality improvement, hypothesizing a positive relationship. Operating costs include maintenance and repair expenses. The regulator sets limits for service continuity and non-technical energy losses in each regulatory period. Service continuity refers to the average length of interruptions in electricity distribution. Non-technical losses refer to losses due to factors specific to the distribution segment. Quality incentives include peer-pressure and penalties/rewards for compliance with minimum quality standards. We model operating costs using a GMM framework to acknowledge endogeneity of variables. The model is dynamic given the inclusion of regulatory lags to recognize past cost behavior. Findings reveal a small trade-off between costs and quality. We conclude that quality improvements are not costly relative to the potential savings from complying with quality standards. We also find that the impact on operating costs is larger when energy losses increase compared to the cost effect due to increases in duration of outages. These findings suggest areas of attention in managerial decision making, and serve as valuable information to the regulator in tailoring quality incentives for this sector.

Unsupervised non-technical losses identification through optimum-path forest

Non-technical losses (NTL) identification has been paramount in the last years. However, it is not straightforward to obtain labelled datasets to perform a supervised NTL recognition task. In this paper, the optimum-path forest (OPF) clustering algorithm has been employed to identify irregular and regular profiles of commercial and industrial consumers obtained from a Brazilian electrical power company. Additionally, a model for the problem of NTL recognition as an anomaly detection task has been proposed when there are little or no information about irregular consumers. For such purpose, two new approaches based on the OPF framework have been introduced and compared against the well-known k-means, Gaussian mixture model, Birch, affinity propagation and one-class support vector machines. The experimental results have shown the robustness of OPF for both unsupervised NTL recognition and anomaly detection problems. In short, the main contributions of this paper are fourfold: (i) to employ unsupervised OPF for non-technical losses detection, (ii) to model the problem of NTL as being an anomaly detection task, (iii) to employ unsupervised OPF to estimate the parameters of the Gaussian distributions, and (iv) to present an anomaly detection approach based on unsupervised optimum-path forest.

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.