Energy Theft Research Articles

Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation

Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation

Unbalanced Data Handling Techniques for Classifying Energy Theft and Defective Meters in the Provincial Electricity Authority of Thailand

Unbalanced Data Handling Techniques for Classifying Energy Theft and Defective Meters in the Provincial Electricity Authority of Thailand

A Novel Combined DenseNet and Gated Recurrent Unit Approach to Detect Energy Thefts in Smart Grids

Due to the illegal use of electricity, non-technical losses are exponentially increasing in electricity distribution systems day by day. With the debut of smart meters in the smart grid, new electricity theft attacks are welcomed. The investigation of abnormal electricity consumption patterns helps in detecting electricity thieves. Moreover, existing methods have poor electricity theft detection (ETD) accuracy due to imbalanced datasets provided by the utilities. They have also failed to capture both periodicity and non-periodicity of 1-D daily electricity usage data. We primarily propose a novel sampling technique to balance the dataset, named as random oversampling using both classes (ROBC). This technique performs oversampling using both the theft and normal classes. With this technique, the problem of low accuracy has been resolved. We also propose a unique ETD model using densenet-fully convolutional network (DenseNet-FCN) and gated recurrent unit (GRU) with a light gradient boosting machine (LightGBM), known as DenseNet-GRU-LightGBM, to address the above mentioned concerns. DenseNet-FCN module extracts periodic and non-periodic patterns from 2-D electricity consumption data in a precise way. Whereas, GRU module captures as well as memorizes features from 1-D consumption data. Afterwards, LightGBM module is used as an ensemble classifier to give final ETD results. As a result, the proposed model has excellent ETD results. Comprehensive simulations indicate that the proposed scheme outperforms other existing methods regarding ETD.

Criminal Characteristics of Criminal Offenses Related to Electricity Theft

This article purpose is to review development state of the issue of forensic characterization of criminal offenses, build the general structure of forensic characteristic of electrical energy theft, determine its main constituent elements, divide them into groups, and develop a detailed forensic characteristic of electric energy theft. Validity of the obtained results and conclusions that were based on the analysis of the latest scientific research on issues of violation of regulatory requirements regarding the accounting of electric energy and unaccounted consumption of electric energy, on the results obtained during performance of forensic examinations at the National Scientific Center »Hon. Prof. M.S. Bokarius Forensic Science Institute« of the Ministry of the Interior of Ukraine, and also ensured the use of a complex of general scientific and special methods, in particular formal and logical, generalization, comparison. Scientific novelty. In the context of forensic expert provision of evidentiary base in criminal act qualification: electric energy theft, provided for in Article 1881 of the Criminal Code of Ukraine, for development of new and supplementing existing methodological recommendations for investigation by employees of pre-trial investigation bodies of criminal offenses; this article elaborates and systematizes data on performing forensic examinations and a detailed forensic description of electric energy theft was compiled that will become the basis for the construction of investigative versions and will contribute to the effective conduct of investigative (search) actions and other investigative measures, as well as quickly. Conclusions. 1. Development state of issue of forensic characteristics of criminal offenses is considered. 2. General structure of forensic characteristics of electric energy theft is built. 3. The main constituent elements of forensic characteristics of electric energy theft are determined. It is proposed to divide constituent elements into groups according to a defining sign; the most significant and auxiliary signs. 4. On the basis of generalizations of data based on the results of forensic examinations and theoretical processing of research results into the issues of unaccounted electric energy consumption, detailed forensic description of electric energy theft has been developed.

Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters

Smart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challenges with respect to the monitoring function. This article presents research results indicating the practical feasibility of real-time detection of anomalies in a road lighting system based on analysis of data from smart energy meters. Short-term time series forecasting was used first. In addition, two machine learning methods were used: one based on an autoregressive integrating moving average periodic model (SARIMA) and the other based on a recurrent network (RNN) using long short-term memory (LSTM). The algorithms were tested on real data from an extensive lighting system installation. Both approaches enable the creation of self-learning, real-time anomaly detection algorithms. Therefore, it is possible to implement them on edge computing layer devices. A comparison of the algorithms indicated the advantage of the method based on the SARIMA model.

Development of effective shielding against electricity meters tampering with strong magnetic fields

• Different ways to locate and orient current transformers on the printed circuit board are considered. • Several rectangular and triangular shielding geometries are simulated and compared in terms of their effectiveness. • Effectiveness of multilayer shields with a gap between layers for flux shunting is estimated. • Recommendations towards effective shielding of the electric energy meters are introduced. To maintain high efficiency, electric utility providers aim to minimize energy losses between points of generation and customer distribution. A significant share of these losses is nontechnical, such as losses from energy theft. One prevalent method of energy theft involves tampering with electricity meters by means of strong magnetic fields. In this study, different shielding structures were developed to protect the electric energy meters’ pulse current transformers from external magnetic fields, with respect to the allocation and orientation of these sensitive elements. Several shielding geometries, solid and multilayer shields were modeled and compared in terms of their efficiency for flux shunting. Based on the obtained results, recommendations for effective shielding against tampering were driven.

Electricity-theft detection for smart grid security using smart meter data: A deep-CNN based approach

Electricity theft has a considerable negative effect on energy suppliers and power infrastructure, leading to non-technical losses and business losses. Power quality deteriorates and overall profitability falls as a result of energy theft. By fusing information and energy flow, smart grids may assist solve the issue of power theft. The examination of smart grid data aids in the detection of power theft. However, the earlier techniques were not very good in detecting energy theft. In this work, we suggested an electricity theft detection approach using smart meter consumption data in order to handle the aforementioned issues and assist and assess energy supply businesses to lower the obstacles of limited energy, unexpected power usage, and bad power management. In specifically, the Deep CNN model effectively completes two tasks: it differentiates between energy that is not periodic and that is, while keeping the general features of data on power consumption. The trial’s results show that the deep CNN model outperforms prior ones and has the best level of accuracy for detecting energy theft.

A Covert Electricity-Theft Cyberattack Against Machine Learning-Based Detection Models

The advanced metering infrastructure in modern networked smart homes brings various advantages, such as multiple pricing and energy scheduling. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of employed ML algorithms. Given a group of smart homes, in this article, we present a covert electricity theft strategy through mimicking normal consumption patterns and compromising neighboring meters concurrently. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we design a feature extraction scheme that can capture the correlation between attacks and honest customers. Finally, we develop a new deep learning-based detection model. Extensive experiments based on real-world datasets show that the presented attack could evade existing mainstream detectors but still gain high profits. In addition, the proposed countermeasure outperforms state-of-the-art detection methods.

Design and Hybrid Simulation of a Larceny Deterrent Energy Evaluation System

The unreliability of the energy system to provide a proper account of energy utilized by consumers has been a huge burden on the distribution system network. Different metering methods and designs to detect and prevent fraud, employed in the past have proven fruitless, thus signalling the need for a much smarter energy metering system. The most frequent problem is electricity larceny, this has incurred a major economic loss in the energy distribution system. To this end, this paper presents the distinctive design and hybrid simulation of a larceny deterrent energy evaluation system, capable of detecting different methods of energy theft within power consumer premises. The method employed comprises of deep understudy of previous work in this field, a model is proposed and is simulated under good working conditions and several theft situations using MATLAB while the hardware is simulated using Proteus 8.1 and Arduino software. In conclusion, the efficiency of the proposed system is evaluated by employing different electric theft algorithms, with the results indicating significant energy cost savings in the distribution network.

Pemberdayaan Pembangkit Listrik Mikro Tenaga Surya dan Sosialisasi Resiko Pencurian Arus PLN di Desa Blang Pulo

At this time, electricity has become a primary need for the community, the existence of the State Electricity Company (PLN) is very supportive of all community activities. The high use of electricity can cause negative effects for users, where the higher the use of electricity, the greater the costs incurred. This is what triggers the emergence of things that are not wanted in society. One of them is the emergence of a desire to steal electric current. In general, people do not know what the risks are due to stealing the electric current. In addition, there are other ways to reduce excessive use of electricity, one of which is by assembling a solar micro-power plant, of which not many people know about the use of assembling the power plant. Based on this, service is carried out with the aim of providing explanations and adding knowledge to the community in the safe use of electrical energy; provide explanations and increase knowledge for the public about the risk of theft of electrical energy; increase community participation in using and protecting the environment by using alternative energy; foster a strong bond between the Blang Pulo gampong which is the closest village to the Malikussaleh University Bukit Indah campus and the Malikussaleh University academic community, especially the electric energy-saving extension team. The counseling activity on the risk of theft of PLN flows in the village of Blang Pulo is carried out in the following activity methods: Program introduction, assembly and delivery of various forms of energy, especially solar energy; The submission of a frugal living attitude according to Islamic law is accompanied by showing several verses of the Koran and hadith and conveying the risks due to stealing electric current. The expected results of this service are through the micro-solar electricity empowerment program and energy-saving counseling, It is hoped that the awareness, sensitivity and participation of lecturers and the community of Blang Pulo will grow and develop in the safe and efficient use of electrical energy so that there is an adequate supply of electricity and there will be no longer rotating blackouts caused by the limited supply of electricity from the PLN, and explain the consequences if electrical energy is stolen for personal or group use in order to be able to use electrical energy for free.

SMART METERING SYSTEM USING AI TECHNIQUES

The economy of energy suppliers and countries may be significantly impacted by non-technical losses (NTLs) in the electrical distribution system, which mostly involve electrical theft. Non-technical losses include consumer dishonesty, unethical transmission line tapping, and hacking or tampering with energy meters. The smart meter, a crucial component of the smart grid, is anticipated to benefit several stakeholders on the economic, social, and environmental levels. Smart meter data analytics, which deals with data gathering, transmission, processing, and interpretation that benefits all stakeholders, is one of the crucial elements that will define the success of smart meters. An artificial neural network can be created in order to find and identify meter manipulation or energy theft. The outcomes can also be applied to larger real-world systems. Additionally, a communication network that satisfies the security requirements for smart grid communication must be properly chosen and implemented in order to deploy smart meters. This article discusses different problems and difficulties related to the development, implementation, use, and upkeep of the smart metering system. Catchphrases- NTL, losses detection, Smart meter, Advanced Metering Infrastructure, Automatic Meter Reading, Neural Networks, Communication Systems, Energy Consumption Profiles.

Synthetic data generation for machine learning model training for energy theft scenarios using cosimulation

AbstractTechnical and non‐technical losses in distribution circuits result in significant economic costs to power utilities. One type of non‐technical loss is energy theft by various means including illegal tapping of feeders, bypassing the meter, and billing fraud. These losses are usually hard to detect, and can remain undetected for long periods of time. Machine learning models have been proven effective in detecting these conditions, but rely on the availability of large, good‐quality training data sets. The problem is exacerbated by the imbalanced nature of data related to these conditions—energy theft, though costly, is very rare. The available data sets generally have very few samples of theft with most of the data pertaining to normal operation. Such data sets are generally not suitable to train machine learning models. In this paper, an overview of energy theft detection techniques, the challenges with their data needs, and the limitations of current techniques to bridge such data limitations is presented. A co‐simulation framework is proposed to generate reliable training data for machine learning algorithms for theft detection. An example scenario is presented and a machine learning model is built to detect certain kinds of energy theft.

Spatio-temporal data-driven detection of false data injection attacks in power distribution systems

The utilization of distributed generation units (DG) in power distribution systems has increased the complexity of system monitoring and operation. Numerous information and communication technologies have been adopted recently to overcome the challenges and complexities associated with the integration of DG units in distribution systems. However, these technologies have created wide opportunities for energy theft and other types of cyber-physical threats. False data injection attacks (FDIA) illustrate a challenging threat for distribution systems for these are very tough to detect in reality. In this manuscript, we propose a spatio-temporal learning algorithm that is able to acquire the normal dynamics of distribution systems to overcome possible FDIA. First, we use a long short-term memory autoencoder (LSTM-AE) in acquiring the usual dynamics. After that, we employ the unsupervised trained model in detecting the numerous potentials of FDIAs in distribution systems by assessing the residual error of every measurement sample. This developed method is purely data-driven. This enables it to be robust to the distribution systems’ nonlinearities and uncertainties which overcomes the weaknesses of the proposed detection algorithms in the literature. The efficacy of the developed detection method is assessed via different test case scenarios with numerous basic and stealth FDIAs.

FedDP: A Privacy-Protecting Theft Detection Scheme in Smart Grids Using Federated Learning

In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data.

Impact of the COVID-19 pandemic on energy islands: The case of Cape Verde

The COVID-19 pandemic and its countermeasures radically affected the energy sector. Within a matter of days, whole countries were into lockdown causing the largest energy impact of the last decades. This study explores the pandemic and its effects on the isolated power systems of Cape Verde, a small island-based developing state in Africa. Historical data from 2013 to 2021 is combined with ARIMA-based forecasting to estimate a COVID-free scenario. The results show how the country’s electricity demand suffered a 10% drop distributed among the islands proportionally to GDP per capita. The energy mix was unaffected, but the lower demand motivated 6% less emissions. The reliability of the system improved with respect previous years, but the transmission losses increased by 5% due to energy theft caused by the severe economic crisis suffered in the archipelago. In that sense, the impact on revenue and energy sector workers was quite limited. Furthermore, we also studied the effects of the pandemic in other energy related sectors such as water desalination and transport. The recovery started in the third quarter of 2020 as marked by the increased electricity demand, but also with the rapid growth of passengers and goods in the transport sector.

Digitally Engineered Attention and Energy Theft via Psychological Manipulation

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Life and evolution</b> , and therefore humans, are dependent on energy. Human evolution and endeavor strive to control more concentrated and versatile forms of energy and to convert them at lower cost and with higher efficiency into heat, light, and motion <xref ref-type="bibr" rid="ref16" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[16]</xref> .

GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection.

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.

Energy Poverty Clustering by Using Power-cut Job Order Data of the Electricity Distribution Companies

The identification of the population suffering from energy poverty, which is more visible after Covid-19 pandemic following by the 2021 energy crisis, is an essential requirement for producing systematic and sustainable solutions. Although European Union approaches to the problem with a multi-indicator sets; this indicator sets have a large amount of secure and almost unreachable data, such as identity information, wage information, health information, asset information (title deed, rental income), expenditure information, debt information, credit information, bank records, etc. Experienced two long term projects between 2014 and 2016 (problem definition for energy theft and the best practices searching 13 different country examples including Brazil, Hungary, India, etc.) and 2016–2018 (energy poverty set and consumption characteristics in Turkey) over 6 million end-user consumption and payment data brings us to confirm that. The primary indicator of energy poverty is the arrears on utility bills. The arrears resulting from the affordability problem of the energy consumed trigger a power cut-off job order in the utility company. This research examines the literature and country social assistance implementation data to see how an energy poverty level can be identified using details on arrears and powercut job orders. On this subject, power-cut job orders were constituted, because of arrears on utility bills, were subjected to statistical analysis, and the compatibility of the trend data with the socio-economic development index was investigated. Cities with a less indexes have more utility bill arrears in terms of both number and volume, according to correlation-test data. Urban cities are more visible in data since the non-urbanized cities have some energy theft activities which show us no efficiency target for the consumption! Hence one of the strategical step for decreasing the non-technical losses is having more registered customer, the relationship between the growth index and the number of customers is another intriguing finding. Separating the consumption levels of arrears, it is found that 63% of total non-payment is depending on 18% of consumers. Trend analysis confirmed that every energy consumption level has the absolute and fluctuated component inside. The number of people inside the absolute poverty cluster is coherent with national and international approaches almost in the same number. The findings revealed that arrears on utility bills can be used specifically to assess the population identified with energy dependency rather than relying on evidence from a variety of sources.

FedDetect: A Novel Privacy-Preserving Federated Learning Framework for Energy Theft Detection in Smart Grid

In smart grids, a major challenge is how to effectively utilize consumers’ energy consumption data while preserving security and privacy. In this article, we tackle this challenging issue and focus on energy theft detection, which is very important for smart grids. Specifically, we note that most existing energy theft detection schemes are centralized, which may be unscalable, and more importantly, may be very difficult to protect data privacy. To address this issue, we propose a novel privacy-preserving federated learning framework for energy theft detection, namely, FedDetect. In our framework, we consider a federated learning system that consists of a data center (DC), a control center (CC), and multiple detection stations. In this system, each detection station (DTS) can only observe data from local consumers, which can use a local differential privacy (LDP) scheme to process their data to preserve privacy. To facilitate the training of the model, we design a secure protocol so that detection stations can send encrypted training parameters to the CC and the DC, which then use homomorphic encryption to calculate the aggregated parameters and return updated model parameters to detection stations. In our study, we prove the security of the proposed protocol with solid security analysis. To detect energy theft, we design a deep learning model based on the state-of-the-art temporal convolutional network (TCN). Finally, we conduct extensive data-driven experiments using a real-energy consumption data set. The experimental results demonstrate that the proposed federated learning framework can achieve high accuracy of detection with a smaller computation overhead.

Review of Modern Electricity Metering Devices

The article gives a description of electricity meters used in the organization of electricity metering. Since 2021 electricity suppliers are required to install meters with a minimum set of functions of intelligent electricity metering systems when installing new meters or replacing old metering devices with new ones, in accordance with Federal Law No. 522-FZ. Outdated induction meters will be replaced by electronic ones with greater reliability, higher accuracy and better protection against theft of electrical energy. The article describes the design of induction and electronic meters, as well as weighs up advantages and disadvantages of each type of device. We also examined smart meters with remote reading.