Theft Detection Research Articles

Identification of theft in a power distribution network: A tree-based approach

Electricity is an essential commodity, yet it is not freely available to everyone in the country due to widespread electricity theft. This theft results in significant financial losses for Distribution Companies (DISCOMS), hindering their ability to provide reliable services to consumers. Although there are methods to curb electricity theft, DISCOMS have struggled to control it effectively. The most successful current method is the manual investigation of nodes; however, it has its drawbacks, such as a low probability of theft detection when nodes are randomly sampled and excessive time consumption when all nodes are investigated. To address these issues, a systematic approach utilizing the capture, storage, and analysis of data is essential. This paper presents a coloring-based tree approach to identify power theft without altering the existing network infrastructure. The proposed approach aims to reduce the dataset for manual inspection, thereby increasing the probability of theft detection while substantially reducing investigation time. By discouraging blind checking of nodes and providing a basis for targeted node inspections, this method benefits both DISCOMS and general consumers. In this paper, the existing methods of theft detection have been compared with the proposed approach of tree-based theft detection. The application of the proposed approach removed certain nodes from the set of nodes to be investigated for theft detection.

Smart Monitoring and Control System for Crude Oil Pipeline Vandalism

Crude oil pipeline vandalism, theft, and unauthorized interference present a significant challenge to the oil industry's security, profitability, and sustainability. Despite initial measures such as security patrols, traditional methods have proven inadequate, particularly in difficult terrains and aquatic regions. These methods often fail to promptly detect and prevent vandalism and theft, which results in significant economic, environmental, and safety risks. This study developed and implemented a smart monitoring and control system to address these challenges by improving the detection of oil pipeline vandalism, including incidents in aquatic environments. The Prototyping methodology was adopted, allowing for continuous testing and improvements throughout the development process. The system was built using C#.NET under the .NET Framework, with Microsoft SQL Server as the database, incorporating an encryption layer for database security. To enhance its effectiveness, an oil theft detection algorithm was integrated with a web-based interface to facilitate real-time monitoring, visualization, and reporting. Testing scenarios demonstrated that the system successfully detected threats and ensured seamless data flow from sensors to the central system. This solution marks a significant advancement in the security and sustainability of oil infrastructure, contributing to environmental protection and the safety of stakeholders

An effective ensemble electricity theft detection algorithm for smart grid

AbstractSeveral machine learning and deep learning algorithms have been presented to detect the criminal behaviours in a smart grid environment in recent studies because of many successful results. However, most learning algorithms for the electricity theft detection have their pros and cons; hence, a critical research issue nowadays has been how to develop an effective detection algorithm that leverages the strengths of different learning algorithms. To demonstrate the performance of such an integrated detection model, the algorithm proposed first builds on deep neural networks, a meta‐learner for determining the weights of detection models for the construction of an ensemble detection algorithm and then uses a promising metaheuristic algorithm named search economics to optimise the hyperparameters of the meta‐learner. Experimental results show that the proposed algorithm is able to find better results and outperforms all the other state‐of‐the‐art detection algorithms for electricity theft detection compared in terms of the accuracy, F1‐score, area under the curve of precision‐recall (AUC‐PR), and area under the curve of receiver operating characteristic (AUC‐ROC). Since the results show that the meta‐learner of the proposed algorithm can improve the accuracy of deep learning algorithms, the authors expect that it will be used in other deep learning‐based applications.

Breaking Water Laws in the Murray‐Darling Basin: Understanding Water Compliance Challenges and Stakeholder Perceptions

AbstractClimate change will put pressure on irrigated agriculture, increasing water scarcity and possible non‐compliance with water regulations. This is especially true in areas such as the Murray‐Darling Basin (MDB), Australia, an area where water resources have been overallocated. Understanding the expected penalties and probability of detection and prosecution of water theft faced by irrigators is important for understanding the incentives for water compliance. We analyze state‐level compliance action data and undertake a detailed case study of estimating the real penalty value of water theft in New South Wales (NSW), the largest MDB state. This analysis is supplemented with qualitative comment on compliance issues from 63 MDB stakeholder interviews. Although findings from random audits suggest that only a small percentage of irrigators commit water theft offenses, the average probabilities of audit detection and prosecution for water theft in NSW between 2018/19 and 2020/21 were low, leading to an average real expected penalty value of stealing water well below existing market prices. Stakeholder interviews confirmed that the majority did not believe there were serious water theft or water compliance challenges—whereas recent secondary survey data suggests that the general public perceives water theft and compliance as a serious and ongoing challenge in the MDB and more needs to be done to address it. Three recommendations for data and policy reform are proposed, namely: (a) improving compliance data and reporting; (b) increasing the probability of detection and prosecution; and (c) increasing penalties, regulator visibility, and reforming legislation. Results illustrate that even countries with highly advanced water governance require data improvements, along with sufficient levels of compliance activities and penalties for effective deterrence and stakeholder confidence.

Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection

Smart grid systems use digital communication technology to efficiently monitor and manage electricity distribution. However, there is a need for improve the robustness of smart grid systems. Hence a novel “Secure Smart Grid Implementation with Automatic Data Integrity Attack Location Prediction and Exalted Energy Theft Detection” has been proposed. Current detection methods are not good at identifying data integrity attacks. So a novel Automatic Deterministic Attack Location Prediction Model detects data integrity attacks using a Stacked Watermarking BiGRU-based Deep Deterministic Q network, extracting system matrix and state time series variation. Moreover, Energy theft detection algorithms, which are intended to detect dishonest clients, provide erroneous findings because of linear interpolation processes and randomised near misses, which ignore important data points. Thus a novel Exalted Energy Theft Detector with Secure Encoding is introduced where Amortized Multicode GAN method secures smart grids by detecting energy theft and preventing firmware code modifications, utilising fractional multi-level quantum encoding to identify injection locations. As a result, the proposed model has an accuracy of 97.1%, recall of 96.25%, error rate is 1.36%, F1-score is 96.20%, sensitivity is 96.10%, specificity is 96.10%, MAPE is 0.48%, MSE is 0.0001 and RMSE is 0.017.

Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction.

The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation.

Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection

Electricity theft is a significant challenge for utility companies worldwide, leading to substantial economic losses and inefficiencies in power distribution. Traditional methods of detecting electricity theft, such as manual inspections and routine audits, are often inefficient and ineffective. To address this issue, this study aims to develop and implement an artificial intelligence (AI)-driven system for electricity theft detection. Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection. To achieve this, a Particle Swarm Optimization Algorithm (PSO) was applied to improve training performance of the SVM, using data of meter recharge information collected from Enugu Electricity Distribution Company (EEDC). The system effectiveness is validated through extensive testing using real-world data from various regions and scenarios, demonstrating its robustness and adaptability. The system result considering FDR reported that 0.11 was achieved for the particle swarm based SVM model. When TPR was considered for analysis, it was observed that particle swarm based SVM attained a score of 0.89. In addition, Particle swarm based SVM attained PPV of 0.895. In terms of accuracy, the particle swarm based SVM reported an accuracy of 0.857. The result showed that the particle swarm based SVM performed better from the system validation achieved through comparative analysis, hence it is recommended for use to develop the new software for energy theft investigation. The implementation of this AI-driven solution offers numerous benefits, including enhanced detection accuracy, reduced operational costs, and improved overall efficiency of power distribution networks. Moreover, it enables utility companies to take proactive measures to prevent theft, ensuring a more reliable and secure electricity supply for consumers.

Machine-learning techniques for enhancing electricity theft detection considering transformer reliability and supply interruptions

Machine-learning techniques are extensively used in fraud detection and have a huge potential application in electricity theft detection. Electricity theft occurs when consumers manipulate their electricity consumption to be recorded as lower than the actual, resulting in reduced revenue for the electricity supplier. This study explores the application of advanced machine-learning techniques to detect electricity theft, focusing on the impact on supply of electricity to electricity usage pattern. We used a dataset that contains supply interruptions influenced by transformer reliability to construct a robust detection model. This model integrates data analytic techniques from decision trees, support vector machines, k-nearest neighbors, and logistic regression classifiers. After selecting the optimal classifier, we used aggregated parameters to improve theft-detection accuracy. A comprehensive sensitivity analysis was used to evaluate the effects of variations in transformer reliability factors on interruption, interruption costs, electricity revenue, and theft-detection performance. Our findings indicate that using optimal aggregated parameters significantly enhances detection accuracy 10.709 percent, 11.435 percent, 4.909 percent, and 2.097 percent from original parameters depend on the number of aggregation in 2-hour, 4-hour, 6-hour, and 12-hour, respectively. The lower transformer reliability leads to increased loss with percentage 66.87 percent, 46.98 percent, 17.22 percent and reduced theft-detection efficiency by 27.32 percent, 19.23 percent, 6.20 percent. depend on reliability factor20 percent, 50 percent, and 80 percent, respectively.

Electricity theft detection model based on the CEEMDAN-CNN-ViT

Abstract Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods.

Research on the identification and detection technology of electricity theft in power distribution areas under a smart grid environment

Abstract With the extensive application of smart meters, power companies can access a wealth of customer electricity usage data, which has not yet been fully utilized. This paper proposes a substation area user identification and electricity theft detection method based on smart meter data, aiming to enhance the level of intelligent management of power grid enterprises and reduce operational costs. By using the grey relational analysis method, analyzing the measurement data collected by smart meters at different time points, it is possible to determine the substation area and phase to which the user belongs and to detect electricity theft behavior. Finally, through simulation, the effectiveness of the proposed method is verified.

Energy Theft Detection and Real-Time Monitoring in a Smart Prepaid Metering System

Energy Theft Detection and Real-Time Monitoring in a Smart Prepaid Metering System

Review on Temporal Convolutional Networks for Electricity Theft Detection with Limited Data

Electricity theft detection using artificial intelligence (AI) and machine learning techniques have shown significant promise in recent research. However, practical implementation and widespread adoption of these advanced methods face several persistent challenges, particularly when dealing with limited data. This review delves into the computational complexity, data requirements, overfitting issues, and scalability and generalizability concerns associated with popular techniques such as Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), Deep Convolutional Neural Networks (DCNN), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and Artificial Neural Networks (ANN). Computational complexity and resource constraints affect the training times and convergence of TCN, LSTM, and DCNN, while high data needs and parameter tuning hinder MLP and GRU. The ANN-based method utilized by the Electricity Company of Ghana underscores overfitting and data duplication, further exacerbated by limited data availability. Moreover, the scalability and generalizability of TCN, LSTM, and DCNN across different regions and larger datasets are limited, with effectiveness varying based on electricity consumption patterns and theft tactics. Addressing these challenges through optimizing computational efficiency, improving data quality and utilization, and enhancing scalability and generalizability is crucial, especially in data-constrained environments. Continued research and development in these areas will be essential for realizing the full potential of AI-based electricity theft detection systems with limited data.

Vehicle Monitoring and Tracking System

In this paper we present a vehicle monitoring and tracking system. Previous systems focus solely on either theft detection or car/driver monitoring. In this paper we provide an integrated solution that provide both theft detection, tracking and driver behavior monitoring. Our system performs well in detecting theft attempts through RFID and tracking location by GPS. It also monitors the driver behavior using OBD reader. Combining both features in one system has great advantages like cost efficiency, space saving and the possibility of adding new features.

Electric theft detection based on multi-source heterogeneous time series GRU network

Abstract Electricity theft has long been one of the major problems faced by power supply enterprises. To improve the robustness and accuracy of power theft detection, this article explores the method of multi-source heterogeneous time series feature fusion and designs a gated cyclic unit network model that adapts to its feature fusion. Firstly, through correlation analysis, it is verified that there is a logical correlation between different time series features and classification features, providing a theoretical basis for feature fusion. Then, an encoder decoder model framework is constructed with an attention mechanism to achieve effective fusion and state detection of user multi-source time series features. The experimental results show that compared to a single data source, the fusion of multi-source features can significantly improve detection performance, and the designed model is superior to the control model. This study provides a reference for constructing an efficient power theft detection system and also provides examples of multi-source heterogeneous feature fusion in related fields.

Electricity Theft Detection Using Rule-Based Machine Leaning (rML) Approach

Since electricity theft affects non-technical losses (NTLs) in power distribution systems, power companies are genuinely quite concerned about it. Power companies can use the information gathered by Advanced Metering Infrastructure (AMI) to create data-driven, machine learning-based approaches for Electricity Theft Detection (ETD) in order to solve this problem. The majority of data-driven methods for detecting power theft do take usage trends into account while doing their analyses. Even though consumption-based models have been applied extensively to the detection of power theft, it can be difficult to reliably identify theft instances based only on patterns of usage. In this paper, a novel rule-based combined machine learning (rML) technique is developed for power theft detection to address the drawbacks of systems that rely just on consumption patterns. This approach makes use of the load profiles of energy users to establish rules, identify the rule or rules that apply to certain situations, and classify the cases as either legitimate or fraudulent. The UEDAS smart business power consumption dataset's real-world data is used to assess the performance of the suggested technique. Our technique is an innovation in theft detection that combines years of intensive theft tracking with the use of rule-based systems as feature spaces for traditional machine learning models. With an astounding 93% recall rate for the rule-based feature space combination of the random forest classifier, this novel approach has produced outstanding results. The acquired results show a noteworthy accomplishment in the field of fraud detection, successfully detecting fraudulent consumers 77% of the time during on-site examination.

Smart Home and Thief Detection using AI

In our world increasingly interconnected, demand for smart home solutions that prioritizing security a convenient ever-growing. This abstract presents an inventive approach to home security an automation through integration of ESP32-CAM, ultrasonic sensor, a Firebase technology. Our system offering real-time theft detecting a remote controlling of home appliances via mobile app. By leveraging AI-drive theft detection a Firebase's cloud-based platform, users can monitor a managing their homes from anywhere, enhancing both security a convenient. This abstract outline component, workflow, features, a potential future enhancement of our Smart Home and Theft Detect system, offering a glimpse into the future of home automation.

Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized.

Smart Energy Meter by Using IOT

Abstract: It is an IOT based paper electricity is one of the fundamental necessities of human beings which is commonly Used for Domestic, Agriculture, Industrial purpose. Power theft and Meter billings are biggest problems in recent days which causes a lot of loss to electricity board. If we can prevent these thefts we can save a lot of power. This is done by Smart Energy Meter by using IOT, present an efficient and cost-effective way to transfer the information of energy consumed by consumer wirelessly as well as it provides facilities to detect illegal usage of electricity. Smart Energy meter by using IOT basically consists of three major components namely controller, Wi- Fi, theft detection device. Whenever there is any fault or theft, the theft detector sensor detects the error. The controller plays a crucial role in keeping all the components in the working state. In this system the energy meter is connected to internet. The proposed system is capable of continuously monitoring and being notified about the number of units consumed to energy provider and consumer. The energy consumption is calculated automatically, and the bill is updated on the internet by using a network of internet of things. This automation can eliminate the manual work in electricity maintenance. In this paper mainly focuses on automatic billing, theft detection, power optimization and providing the relevant energy consumption information to user.

An IOT Based Vehicle Theft Detection and Remote Engine Locking System

Abstract: An IoT based vehicle theft detection and remote engine locking system is GSM (Global System for Mobile Communication) technology that helps the users to identify the vehicle in the theft mode and allow you to use the controlling mechanism technique. These days, there are a ton of cars being stolen. It's a big problem because crime rates are going up, and it's making it harder to keep vehicles safe. Criminals are becoming smarter and have reached the stage of applications present against the vehicles safety system. Vehicle theft has become a considerable issue which should be traced and prevented. The new system solves many problems and is affordable. Also the proposed system reducing complications by making use of some high priced products like ignition key, microcontroller. In the proposed method, we include an additional feature where controlling mechanisms remotely lock the vehicle engine and prevent theft. In proposed technique, user can start/stop the vehicle either by using the ignition key, which tracks exact location of the vehicle using the application. This paper talks about a great anti-theft system for vehicles. It uses Global Positioning System(GPS),Global System for Mobile Communication(GSM),and Biometrics technologies(i.e fingerprint ) to find the vehicle and confirm the owner's identity. Fingerprint device captures the fingerprints and matches the distinctiveness of every print scan by the device and compares it to the one keeps in its module or native system database.

Smart Energy Meter with Advanced Billing and Power Theft

The remote energy monitoring system is a meter and IOT software combo that provides amenities for the consumer. Indonesia swiftly embraced smart metering over traditional metering. India still uses the conventional type, which has issues such as electromechanical meter errors, human errors, and processing flaws. Using an ATMEGA 328p and a node MCU Micro Controller, we constructed a power theft detection and invoicing system for this project. It contains tools to monitor power in real-time using Internet of Things technology and is capable of automatically generating power bills. Additionally, in addition to alerting the user when power changes such as undervoltage, overvoltage, or overload occur, our system also looks for instances of power theft. The Power Theft Detection and Power Billing System is a ground-breaking initiative that was created as a result of the energy management industry's unwavering quest of innovation. This project represents the future of intelligent energy management since it is based on the confluence of technology and pragmatism