Electricity Theft Detection and Localization in Smart Grids for Industry 4.0

Wisetsri, Worakamol; Qamar, Shamimul; Verma, Gaurav; Verma, Deval; Kakar, Varun Kumar; Chansongpol, Thanyanant; Somtawinpongsai, Chanyanan; Tan, Chai Ching

doi:10.32604/iasc.2022.024610

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…However, few works have been found that balance the data distribution. G. 4 Only one study [32] applied a deep learning model but without any feature selection or data sampling techniques. Moreover, this system classified only 6 classes.…”

Section: Cic-ids2017 Related Workmentioning

confidence: 99%

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Alshede¹,

Nassef²,

Alowidi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Advanced Metering Infrastructure (AMI) is the metering network of the smart grid that enables bidirectional communications between each consumer's premises and the provider's control center. The massive amount of data collected supports the real-time decision-making required for diverse applications. The communication infrastructure relies on different network types, including the Internet. This makes the infrastructure vulnerable to various attacks, which could compromise security or have devastating effects. However, traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks. The objective of this paper is to develop an Anomaly Detection System (ADS) based on deep learning using the CIC-IDS2017 dataset. However, this dataset is highly imbalanced; thus, a two-step sampling technique: random under-sampling and the Synthetic Minority Oversampling Technique (SMOTE), is proposed to balance the dataset. The proposed system utilizes a multiple hidden layer Auto-encoder (AE) for feature extraction and dimensional reduction. In addition, an ensemble voting based on both Random Forest (RF) and Convolutional Neural Network (CNN) is developed to classify the multiclass attack categories. The proposed system is evaluated and compared with six different state-ofthe-art machine learning and deep learning algorithms: Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGboost), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and bidirectional LSTM (biLSTM). Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy (98.29%), precision (99%), recall (98%), F 1 score (98%), and the UNDetection rate (UND) (8%).

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Section: Cic-ids2017 Related Workmentioning

confidence: 99%

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Alshede¹,

Nassef²,

Alowidi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…In addition, the precise location of theft is displayed on the webpage using the geolocation application programming interface (API) on Google Maps. The proposed system can be easily integrated into the smart grid network structure, helping enterprises increase their business [5].…”

Section: Introductionmentioning

confidence: 99%

Development of new pocket distribution network inspection equipment

Lin,

Su,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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This paper aims to explore a new intelligent inspection mode of pocket distribution network equipment, which realizes rapid and comprehensive inspection and inspection by using terminal +N intelligent sensors and partial discharge detection and infrared temperature measurement technology as the core. First, this paper introduces the background and motivation of the new mode, and emphasizes the limitations of the existing traditional inspection methods and the problems that need to be improved. Then, the design and manufacture of pocket devices are described in detail, including key aspects such as sensor integration, portability and user-friendliness. Finally, the paper summarizes the research results and emphasizes the potential benefits and future research directions of this new intelligent inspection model.

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