Electricity Theft Research Articles

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.

Energy Security of Hydropower Producing Countries—The Cases of Tajikistan and Kyrgyzstan

Energy security, as one of the most important components of state security, is a permanent element of academic debates and political discussions. Owing to the multidimensional and multifaceted nature of energy security, defining it is a complex process, requiring the consideration of a wide range of factors straddling economics, geology, ecology and geopolitics which decide whether we are dealing with the state of energy security or the lack of it. Energy security is usually equated with the security of supply. Another group of definitions of energy security focuses on the concept of security of services. A different approach to energy security issues is presented by energy exporting countries, whose objective is to ensure sufficiently high and stable income from sales of energy resource exports (security of demand). The subject of this paper is an analysis of the energy security of hydropower-producing countries—Tajikistan and Kyrgyzstan. Energy security has been analyzed in the context of security of supply, services, and demand on the basis of the approach proposed by Llamosas and Sovacool. So far, no work has been carried out to analyse the hydropower sectors of Kyrgyzstan and Tajikistan in the context of the energy security comparison of both countries. It is worth emphasising that their energy security and mutual relations are important from the point of view of the stability of the entire Central Asian region. Kyrgyzstan and Tajikistan have large hydropower potential, which, if properly used, could cover their domestic electricity demand and the surplus can be exported to neighbouring countries. Unfortunately, both countries are not utilising this potential for the time being. The main difficulty in the area of security of supply and services is the seasonality and low reliability of electricity supplies. Among the reasons for this are the poor technical and economic conditions of energy companies as a result of maintaining low tariffs, the irrecoverability of consumers’ energy bills, electricity theft, significant transmission loses and a high level of corruption. Although both countries aspire to the role of an energy exporters, they are themselves forced to import electricity from neighboring countries.

A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC.

Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU-BiLSTM Model with Feature Engineering-Based Preprocessing.

In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors.

An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis

Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675).

A novel approach to detect electricity theft based on conv-attentional Transformer Neural Network

With the establishment of advanced metering infrastructure (AMI), more and more deep learning (DL) methods are being used for electricity theft detection. However, there are still problems such as the construction of a DL model that better fits the theft detection task, sample imbalance in the data set and overfitting of the model which limit the potential of DL models. Therefore, a novel end-to-end solution based on DL to solve these problems for electricity theft detection is proposed in this paper. First, we construct a model based on Transformer Neural Network (TNN), which extracts global features of consumption data by calculating the self-attention between each segment obtained by dividing the whole load sequence, and calculates the relative relationship between features for customer classification. Then, we refine the model to further improve its performance. Conv-attentional module is used to embed the input data and capture the local features in each segment. And we minimize the effect of sample imbalance by choosing a suitable loss function, and address the problem of model overfitting by adding normalization layers, dropout regularization and L2 regularization. In addition, grid search is used to determine the optimal values of the model hyper-parameters. Finally, the performance of the proposed model is verified by experiments using the Irish data set. The results show that our method is able to extract features more efficiently and thus has a higher true positive rate (TPR) with a lower false positive rate (FPR) than other state-of-the-art detectors, and it has strong robustness.

The detection of non-technical losses and electricity theft by smart meter data and Artificial Intelligence in the context of electric distribution utilities: A comprehensive review

The detection of non-technical losses and electricity theft by smart meter data and Artificial Intelligence in the context of electric distribution utilities: A comprehensive review

Development of an Electricity Theft Detection System

Electricity theft stays a gigantic misfortune brought about by electricity appropriation organizations. This robbery emerges significantly increased when this is done by consumers. For example, meter bypassing, tempering meter, and so on. The main reason for the losses is the direct connection of distribution lines which means Kunda or hooking from electricity lines. So, an intensive method is required to stop this kind of electricity theft. This examination study offers a methodology for taking care of electricity meter bypassing and altering. The overall system is a design based on a GSM module, Arduino Mega, Arduino Nano, and three current sensors, in which one current sensor measures the incoming current from the main grid and the other two sensors measure the current from the consumption side and compare the incoming and consumption current values. When there is any value increase from the compared values, it will detect electricity theft. This method will automatically read and send the current sensor values and also detect electricity theft.

Of cooks, crooks and slum-dwellers: Exploring the lived experience of energy and mobility poverty in Mexico's informal settlements

So-called “slum-dwellers” living in informal settlements in Mexico City Metropolitan Area (MCMA) often confront poor health outcomes, face chronic accommodation insecurity and are frequent victims of social intolerance, discrimination and racism. In addition, they usually reside in living environments with precariously hazardous conditions that often lead to their well-being endangerment. Based on extensive original research with slum-dwellers from the MCMA including focus groups (N = 18 participants), household interviews (N = 51 participants), and site visits (N = 5), this study investigates their energy and housing needs, transport and mobility patterns and challenges to their overall quality of life and health. The MCMA is one of the largest metropolitan regions globally, and most of its inhabitants experience a “double energy vulnerability,” circumstances whereby people are at an intensified risk of energy and transport poverty simultaneously. Our investigation circles around three key themes. In exploring the subject of extreme poverty and vulnerability, we show not only the problems they confront but also illegal practices such as electricity thefts and coping strategies. In investigating the subject of perpetual peripheralization, we show troubling patterns of discrimination, racism and social intolerance. In exploring the subject of spatial justice, we suggest a set of policies that ought to help achieve it.

Electricity theft detection in unbalanced sample distribution: a novel approach including a mechanism of sample augmentation

Electricity theft detection in unbalanced sample distribution: a novel approach including a mechanism of sample augmentation

Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids

Designing an electricity theft cyberattack detector for the advanced metering infrastructures (AMIs) is challenging due to the limited availability of electricity theft datasets (i.e., malicious datasets). Anomaly detectors, which are trained solely on honest customers’ energy consumption profiles (i.e., benign datasets), could potentially overcome this challenge. Unfortunately, existing anomaly detectors in AMIs present shallow architectures and are incapable of capturing the temporal correlations as well as the sophisticated patterns present in the electricity consumption data, which impact their detection performance negatively. This article proposes the adoption of deep (stacked) autoencoders with a long-short-term-memory (LSTM)-based sequence-to-sequence (seq2seq) structure. The depth of the autoencoders’ structure helps capture the sophisticated patterns of the data and the seq2seq LSTM model enables exploitation of the time-series nature of data. We investigate the performance of simple autoencoder, variational autoencoder, and autoencoder with attention (AEA), in which improved detection performance is observed when seq2seq structures are adopted compared to fully connected ones. Based on the simulation results, the AEA detector offers an enhancement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4\!-\!21\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4\!-\!13\%$</tex-math></inline-formula> in terms of detection rate and false alarm rate, respectively, compared to existing state-of-the-art detectors

Electricity Theft Detection Based on ReliefF Feature Selection Algorithm and BP Neural Network

As China’s distributed energy is still in the development stage, energy transmission loss will inevitably occur in the transmission process from the source end to the load end. To reduce transmission energy loss, we should also beware of electricity theft. The principle of common electricity theft methods is analyzed to improve the accuracy of established electricity theft characteristics and electricity theft detection. The ReliefF multivariate characteristics selection algorithm optimizes the electricity theft characteristics. The back propagation (BP) neural network-based electricity theft detection model is built, and the optimized characteristics are selected as the model’s input. The experiment results show that the detection model has better electricity theft identification accuracy using the optimized characteristics for electricity theft detection.

On the effect of sampling frequency on the electricity theft detection performance

Electricity theft is a common problem in many countries and affects the revenue of the electricity industry significantly. Recently, machine and deep learning techniques are being used widely to detect thieves by analysing the consumption patterns. While the prediction accuracy of these methods depends on the number and quality of the existing samples used for training models, the majority of previous research work focussed on data with high sampling frequencies, for example, data from smart grids. However, based on technological and operational limitations, the rate of metre reading is too low in many countries, which can affect detection performance. To investigate the effect of sampling frequency on the performance of detection methods, we designed a processing framework to evaluate different classification algorithms on versions of a challenging dataset obtained by down-sampling the original data at various rates. We used different features from time, frequency and time-frequency domains and their combinations in our investigations. Experimental results show that the performance of the models decreases with a small slope up to the sampling frequency of one data per week. The performance degradation rate is significant for data with lower sampling frequencies. We also proposed a bagging network that could improve the overall detection performance at different sampling frequencies.

Consumer's perception towards electricity theft: A case study of Islamabad and Rawalpindi using a path analysis

Electricity theft is a chronic issue and energy worth billions of dollars is stolen annually from electricity grids mainly in the developing countries. This study highlights socio-economic and institutional factors deteriorating the electricity theft situation using the path analysis by employing a measurement model in the analysis of a moment structures (AMOS). Preceding the path analysis, statistical package for social sciences (SPSS) is used for descriptive statistics, reliability analysis through Cronbach's alpha test, and exploratory factor analysis through KMO & Barlett's test of sphericity. The study analyses the factors that facilitate the illegal consumption of electricity in the service area of Islamabad Electric Supply Company, Pakistan, utilizing a primary dataset collected through a structured questionnaire from the twin cities of Islamabad and Rawalpindi. The analysis is based on perception of the consumers. The factors that significantly and positively affect the behavior towards theft include corruption diluting deterrence and power outages/load shedding, whereas the age of the respondent negatively affects their perception towards electricity theft. The role of electricity tariff rate, electricity consumption pattern, probability of detection, the rule of law, education of the respondent and quality of conduct of utility officials appeared insignificant in motivating consumers to steal electricity.

Comparative Research on the Techniques of Electricity Fraud Detection Using Different Machine Learning Techniques

The vulnerability of power theft has hampered the electricity industry for decades. It obstructs social progress by having varying degrees of impact on home, commercial, and industrial customers. Sneak thieves have caught up with contemporary metering systems, putting electricity suppliers in trouble financially. This comparative analysis is the first step in the presentation of principles. Theft of electricity has serious consequences for the power grid's proper operation as well as the economic benefits of power corporations and commercial power service providers. An effective anti-power-theft algorithm is required for tracking power usage statistics in order to detect electricity power theft. In this literature review, we differentiate the Support Vector Machine (SVM) algorithm with other techniques for detecting abnormal usage among consumers (i.e., electricity fraudsters) in time-series data on power consumption. The results show some combinations can reach significantly better values than others, comparing both the balancing techniques for a same machine learning method itself as well as comparing these combinations between themselves.

Electricity theft Detection with Borderline-SMOTE and WDCBL in the Smart Grid

The non-technical loss caused by electricity theft on the user side not only increases the operationcost of the smart grid but also causes some harm to the electricity system. At present, the existingelectricity theft detection model ignores the time-series correlation of user power consumption data,and the distribution of positive and negative samples in the relevant data sets is uneven. Aiming atthe existing problems, we propose an electricity theft detection model based on Borderline-SMOTEand WDCBL(wide and deep CBL) networks. The WDCBL model consists of wide componentsand deep CBL components. The wide part uses a fully connected layer to extract one-dimensionalfeatures in user electricity consumption data. The model introduces the hybrid network model ofa one-dimensional convolutional neural network(CNN) and bidirectional long-term and short-termmemory network(BiLSTM) into the deep part, which can learn both spatial and temporal features inuser electricity consumption data. Finally, we conducted a comparative experiment on the user electricityconsumption data published by SGCC. The results show that our model is superior to othersin the comprehensive performance.

Houselessness, Infrastructural Exclusion, and Stigmatization

At the feet of the Serra da Cantareira forest in São Paulo, land grabbers illicitly seize and sell land to houseless people. In 2019, I conducted fieldwork in a newly established squatter camp along the Tremembé River, inhabited mainly by Black and Brown women who had migrated from rural Brazil. Since they are considered illegal occupants by the authorities and live in an area at risk of flooding, they may soon be evicted without compensation. The criminalization of these houseless people by the government overlaps with the stigma attached to them by the residents of nearby settlements. They associate the squatters with alleged disruptive practices against nature (deforestation, pollution, and garbage accumulation), theft of electricity, and appropriation of federal subsidies. Stemming from hygienist discourses, racialized and gendered ideologies shape this environmental imagination.

Pattern-based and context-aware electricity theft detection in smart grid

The smart grid offers numerous potential benefits for energy management by two-way information transmission. However, the incorporation of smart infrastructure such as smart meters into the grid systems made it vulnerable to various communication attacks. The electricity consumption data coming from the smart meters can be acquired and analyzed to detect malicious behavior, tampering, and data manipulation. In this paper, a pattern-based and context-aware approach for electricity theft detection (PCETD) is proposed to mitigate the challenges of theft-based Non-Technical Losses (NTLs). The proposed approach considers the relevant calendar context and features of daily electricity demand for a given day to compute the probability of customers being malicious. In this process, a combination of dynamic time warping (DTW) and k-nearest neighbors (k-NN) is proposed, where DTW is utilized to capture the relationship between two consumption patterns correctly, and k-NN is employed to rank the anomalousness of the given day. This research introduces several forms of theft attacks and assesses the effectiveness of the proposed approach. The results exhibited the overall F1-score for the proposed approach to be 94%, the True-Positive Rate (TPR) as 93%, and the False-Positive Rate (FPR) as 1.1%, which substantiates the efficiency of the model in detecting electricity theft. These findings further advocate that the approach outperforms the prior contributions in terms of high detection rate and low false-positive rate. Electric utilities can be provided with anomaly scores and plan on-site inspections to catch fraudulent customers.

Development of smart grid for the power sector in India

Technological advancements in the energy industry have expedited the growth of the smart grid, necessitating multidisciplinary study in power systems and management. India, the world's third-largest producer and consumer of electricity, has various power-related issues, including significant transmission and distribution losses, electricity theft, and environmental concerns. As a result of these issues, the energy industry is exploring new technologies to improve the grid's efficiency, sustainability, and security. This study tries to uncover elements that might be facilitators for India's smart grid development. To analyse the various components of the smart grid, questionnaire surveys, interviews, and workshops were conducted with eminent academicians, researchers, and industry experts working in the power sector. An integrated approach of soft system methodology (SSM), thematic analysis and fuzzy cognitive mapping have been used in this study to better comprehend the intricate interactions between stakeholders. This research findings reveal that technical development along with consumer acceptance is a crucial factor for the efficacious implementation of smart grid.

Edge Computing Based Electricity-Theft Detection of Low-Voltage Users

Electricity theft of low voltage (LV) users could result not only in the escalation of power loss but also in dangerous electric shock. Since LV users are served by distribution transformers, electricity theft of an LV user will cause line loss escalation of the associated distribution serving zone (DTSZ). Therefore, it seems promising to identify anomaly users of electricity theft with a Granger causality test to find out the user causing an escalation of line loss in DTSZ with time series of users’ usage and line loss. However, meters of LV users in severe environments occasionally suffer from communication failure to upload metering data to the head end of advanced metering infrastructure (AMI), which could distort the daily electricity usage of the associate user. Consequently, it could cause false alarms unavoidably once we detect electricity theft with these distorted data. Since the distribution transformer unit (DTU) collects metering data of LV users within associate DTSZ without distortion, an edge computing–based electricity theft detection approach is proposed in this article. The correlation between line loss of a DTSZ and electricity usage of anomaly users of electricity theft is first analyzed. Thereafter, the Granger causality test is used to identify anomaly users with authentic usage data with edge computing in DTU. Finally, the abnormal data and the data repaired by different missing data filling algorithms are used on the main station to detect electricity theft. Numerical simulation suggests that although missing data completion could recover information in missing data partially, it could result in notable false positive alarms in electricity theft, while the proposed method based on edge computing can completely eliminate the data distortion caused by communication failure.