717 research outputs found
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
Non-technical losses (NTL) such as electricity theft cause significant harm
to our economies, as in some countries they may range up to 40% of the total
electricity distributed. Detecting NTLs requires costly on-site inspections.
Accurate prediction of NTLs for customers using machine learning is therefore
crucial. To date, related research largely ignore that the two classes of
regular and non-regular customers are highly imbalanced, that NTL proportions
may change and mostly consider small data sets, often not allowing to deploy
the results in production. In this paper, we present a comprehensive approach
to assess three NTL detection models for different NTL proportions in large
real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and
Support Vector Machine. This work has resulted in appreciable results that are
about to be deployed in a leading industry solution. We believe that the
considerations and observations made in this contribution are necessary for
future smart meter research in order to report their effectiveness on
imbalanced and large real world data sets.Comment: Proceedings of the Seventh IEEE Conference on Innovative Smart Grid
Technologies (ISGT 2016
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
An Effective LSTM-DDPM Scheme for Energy Theft Detection and Forecasting in Smart Grid
Energy theft detection (ETD) and energy consumption forecasting (ECF) are two
interconnected challenges in smart grid systems. Addressing these issues
collectively is crucial for ensuring system security. This paper addresses the
interconnected challenges of ETD and ECF in smart grid systems. The proposed
solution combines long short-term memory (LSTM) and a denoising diffusion
probabilistic model (DDPM) to generate input reconstruction and forecasting. By
leveraging the reconstruction and forecasting errors, the system identifies
instances of energy theft, with the methods based on reconstruction error and
forecasting error complementing each other in detecting different types of
attacks. Through extensive experiments on real-world and synthetic datasets,
the proposed scheme outperforms baseline methods in ETD and ECF problems. The
ensemble method significantly enhances ETD performance, accurately detecting
energy theft attacks that baseline methods fail to detect. The research offers
a comprehensive and effective solution for addressing ETD and ECF challenges,
demonstrating promising results and improved security in smart grid systems
SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
This paper presents the development of a Supervisory Control and Data
Acquisition (SCADA) system testbed used for cybersecurity research. The testbed
consists of a water storage tank's control system, which is a stage in the
process of water treatment and distribution. Sophisticated cyber-attacks were
conducted against the testbed. During the attacks, the network traffic was
captured, and features were extracted from the traffic to build a dataset for
training and testing different machine learning algorithms. Five traditional
machine learning algorithms were trained to detect the attacks: Random Forest,
Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained
machine learning models were built and deployed in the network, where new tests
were made using online network traffic. The performance obtained during the
training and testing of the machine learning models was compared to the
performance obtained during the online deployment of these models in the
network. The results show the efficiency of the machine learning models in
detecting the attacks in real time. The testbed provides a good understanding
of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin
Mitigating Anomalous Electricity Consumption in Smart Cities Using an AI-Based Stacked-Generalization Technique
Energy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non-technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the main issue in maintaining grid stability and cause revenue loss to the utility. The automatic metering infrastructure (AMI) system has reduced grid instability but it has opened up new ways for NTLs in the form of different cyber-physical theft attacks (CPTA). Machine learning (ML) techniques can be used to detect and minimize CPTA. However, they have certain limitations and cannot capture the energy consumption patterns (ECPs) of all the users, which decreases the performance of ML techniques in detecting malicious users. In this paper, we propose a novel ML-based stacked generalization method for the cyber-physical theft issue in the smart grid. The original data obtained from the grid is preprocessed to improve model training and processing. This includes NaN-imputation, normalization, outliers\u27 capping, support vector machine-synthetic minority oversampling technique (SVM-SMOTE) balancing, and principal component analysis (PCA) based data reduction techniques. The pre-processed dataset is provided to the ML models light gradient boosting (LGB), extra trees (ET), extreme gradient boosting (XGBoost), and random forest (RF), to accurately capture all consumers\u27 overall ECP. The predictions from these base models are fed to a meta-classifier multi-layer perceptron (MLP). The MLP combines the learning capability of all the base models and gives an improved final prediction. The proposed structure is implemented and verified on the publicly available real-time large dataset of the State Grid Corporation of China (SGCC). The proposed model outperformed the individual base classifiers and the existing research in terms of CPTA detection with false positive rate (FPR), false negative rate (FNR), F1-score, and accuracy values of 0.72%, 2.05%, 97.6%, and 97.69%, respectively
Review of Non-Technical Losses Identification Techniques
Illegally consumption of electric power, termed as non-technical losses for the distribution companies is one of the dominant factors all over the world for many years. Although there are some conventional methods to identify these irregularities, such as physical inspection of meters at the consumer premises etc, but it requires large number of manpower and time; then also it does not seem to be adequate. Now a days there are various methods and algorithms have been developed that are proposed in different research papers, to detect non-technical losses. In this paper these methods are reviewed, their important features are highlighted and also the limitations are identified. Finally, the qualitative comparison of various non-technical losses identification algorithms is presented based on their performance, costs, data handling, quality control and execution times. It can be concluded that the graph-based classifier, Optimum-Path Forest algorithm that have both supervised and unsupervised variants, yields the most accurate result to detect non-technical losses
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