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Efficient Fault Detection and Categorization in Electrical Distribution Systems Using Hessian Locally Linear Embedding on Measurement Data
First, we employ HLLE to transform high-dimensional (HD) electrical data into low-dimensional (LD) embedding coordinates.
Fault Classification in Electrical Distribution Systems using Grassmann Manifold
We use simulated data for electrical distribution systems with various types of electrical faults.
Regression Based Anomaly Detection in Electric Vehicle State of Charge Fluctuations Through Analysis of EVCI Data
With the increase in the number of electric vehicles (EV), there is a need for the development of the EV charging infrastructure (EVCI) to facilitate fast charging, thereby mitigating the EV congestion at charging stations.
Energy Sharing among Resources within Electrical Distribution Systems: A Systematic Review
Also, the possibility of energy sharing in electrical distribution systems for microgrids and EVCI on various energy-sharing methods and algorithms are discussed in detail.
Advancements in Arc Fault Detection for Electrical Distribution Systems: A Comprehensive Review from Artificial Intelligence Perspective
This comprehensive review paper provides a thorough examination of current advancements and research in the field of arc fault detection for electrical distribution systems.
Modelling of the Electric Vehicle Charging Infrastructure as Cyber Physical Power Systems: A Review on Components, Standards, Vulnerabilities and Attacks
The increasing number of electric vehicles (EVs) has led to the growing need to establish EV charging infrastructures (EVCIs) with fast charging capabilities to reduce congestion at the EV charging stations (EVCS) and also provide alternative solutions for EV owners without residential charging facilities.
DynamoPMU: A Physics Informed Anomaly Detection and Prediction Methodology using non-linear dynamics from $μ$PMU Measurement Data
The expansion in technology and attainability of a large number of sensors has led to a huge amount of real-time streaming data.
Identification of Forced Oscillation Sources in Wind Farms using E-SINDy
The rapid growth of wind power generation has led to increased interest in understanding and mitigating the adverse effects of wind turbine wakes and forced oscillations in wind farms.
CerviFormer: A Pap-smear based cervical cancer classification method using cross attention and latent transformer
This study proposes a cross-attention-based Transfomer approach for the reliable classification of cervical cancer in Pap smear images.
A Resilient Power Distribution System using P2P Energy Sharing
The adoption of distributed energy resources (DERs) such as solar panels and wind turbines is transforming the traditional energy grid into a more decentralized system, where microgrids are emerging as a key concept.
Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Price
Using the proposed method, the results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs with a 50kWh charger rating) resulting in a minimum power loss of 201. 40 kW and squared voltage deviations of 0. 0182 p. u.
Detection of Malfunctioning Modules in Photovoltaic Power Plants using Unsupervised Feature Clustering Segmentation Algorithm
In this paper, we use an unsupervised deep-learning image segmentation model for the detection of internal faults such as hot spots and snail trails in PV panels.
Machine Learning as an Accurate Predictor for Percolation Threshold of Diverse Networks
The percolation threshold is an important measure to determine the inherent rigidity of large networks.
Evaluation of Energy Resilience and Cost Benefit in Microgrid with Peer-to-Peer Energy Trading
Several researchers have developed models to implement energy trading in the P2P network with DERs that enables prosumers to avail cost benefits.
Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model
The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants.
Local Interpretable Model Agnostic Shap Explanations for machine learning models
There are variety of frameworks using explainable AI (XAI) methods to demonstrate explainability and interpretability of ML models to make their predictions more trustworthy.
Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory
For these correlated networks, computed the network parameters such as clustering coefficient, assortative coefficient, average degree and power law exponent for the anticipation; and percolation threshold for the determination of the network withstand capability under extreme conditions.
Data-Driven Approach to form Energy Resilient Smart Microgrids with Identification of Vulnerable Nodes in Active Electrical Distribution Network
The results show a 20. 45% improvement in the resilience of the system due to the optimal allocation of DERs.
