Meta-learning based voltage control strategy for emergency faults of active distribution networks
With the increase of energy demand and the continuous development of renewable energy
technology, active distribution networks have become increasingly important. However, the …
technology, active distribution networks have become increasingly important. However, the …
Electricity theft detection using Euclidean and graph convolutional neural networks
The widespread penetration of advanced metering infrastructure brings an opportunity to
detect electricity theft by analyzing the electricity consumption data collected from smart …
detect electricity theft by analyzing the electricity consumption data collected from smart …
ETD-ConvLSTM: A deep learning approach for electricity theft detection in smart grids
X Xia, J Lin, Q Jia, X Wang, C Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In smart grids, various Internet-of-Things-based (IoT-based) components are massively
deployed across the power systems. However, most of these IoT-based components have …
deployed across the power systems. However, most of these IoT-based components have …
Electricity theft detection using deep reinforcement learning in smart power grids
AT El-Toukhy, MM Badr, MMEA Mahmoud… - IEEE …, 2023 - ieeexplore.ieee.org
In smart power grids, smart meters (SMs) are deployed at the end side of customers to report
fine-grained power consumption readings periodically to the utility for energy management …
fine-grained power consumption readings periodically to the utility for energy management …
Electricity theft detection based on contrastive learning and non-intrusive load monitoring
A Gao, F Mei, J Zheng, H Sha, M Guo… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Electricity theft has caused enormous damage to grid's safety and economy globally,
bringing plentiful attention to electricity theft detection. However, the inherent problems of …
bringing plentiful attention to electricity theft detection. However, the inherent problems of …
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 …
presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both …
RFE based feature selection and KNNOR based data balancing for electricity theft detection using BiLSTM-LogitBoost stacking ensemble model
Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced
metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues …
metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues …
Employing a machine learning boosting classifiers based stacking ensemble model for detecting non technical losses in smart grids
In the modern world, numerous opportunities help detect electricity theft happening in
electricity grids due to the widespread shifting of people from old metering infrastructure to …
electricity grids due to the widespread shifting of people from old metering infrastructure to …
Countering evasion attacks for smart grid reinforcement learning-based detectors
Fraudulent customers in smart power grids employ cyber-attacks by manipulating their smart
meters and reporting false consumption readings to reduce their bills. To combat these …
meters and reporting false consumption readings to reduce their bills. To combat these …