Anomaly detection with grid sentinel framework for electric vehicle charging stations in a smart grid environment

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 6, 2025

Electric vehicle (EV) charging stations on the smart grid are needed to promote electric car adoption and sustainable transportation. The key issues lack of continuous monitoring incident response, difficulty linking systems with EV stations, security gaps that may not address particular vulnerabilities. Modern measures protect from those attacks, which cause significant disruptions. Machine Learning Empowered Anomaly Detection Grid Sentinel Framework (AD-GS) is proposed safeguard against intrusions. This technology can also detect respond suspicious movements dynamically using powerful machine learning algorithms (long short-term memory (LSTM), random forest, autoencoder models), ensuring safety. testing findings reveal automatically updated neutralize threats quickly, utilizing dynamic methods minimize downtime. method increases safety be applied beyond stations. AD-GS architecture tested in simulations shown resilient extraordinary no impact station performance. simulation showed could reduce downtime by implementing quick threat mitigation, improve response time efficiency 98.4%, abnormalities 96.8% accuracy. framework protects user operation data 99.2% time. Extended monitor more than 500 distribution networks, substations,

Language: Английский

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Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network

Energies, Journal Year: 2024, Volume and Issue: 17(12), P. 2959 - 2959

Published: June 16, 2024

In the field of intelligent transportation, planning traffic flows that meet energy-efficient driving requirements necessitates acquisition energy consumption data for each vehicle within flow. The current methods calculating generally rely on longitudinal dynamics models, which require comprehensive knowledge all power system parameters. While this approach is feasible individual it becomes impractical a large number types. This paper proposes digital model using speed, acceleration, and battery state charge (SOC) as inputs output. trained an optimized CNN-BiLSTM-Attention (OCBA) network architecture. comparison to other methods, OCBA-trained predicting PHEV more accurate in simulating time-dependency between SOC instantaneous fuel consumption, well distribution relationship PHEVs. provides excellent framework modeling complex systems with multiple sources. requires only 54 tests training, significantly fewer than over 2000 typically needed obtain parameters components. model’s prediction error under unknown conditions reduced 5%, outperforming standard benchmark 10%. Furthermore, demonstrates high generalization capability R2 value 0.97 conditions.

Language: Английский

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Assessing Reliability and Economic Viability of Different EV Charging Station Configurations

Qeios, Journal Year: 2024, Volume and Issue: unknown

Published: March 5, 2024

With the increasing popularity of electric vehicles (EVs) as a mode transportation, companies are prioritizing development charging infrastructure to cater customer needs. Despite efforts align station designs with distribution system requirements, maintaining reliability for EV ports remains challenging. To enhance reliability, novel 56-ported design incorporating both uniform and non-uniform port arrangements has been proposed. These configurations have tested systems ranging from 50 500 kW. Reliability assessments were conducted using standards outlined failure rate estimation monte-carlo functions evaluating probability in terms reliability. By analyzing rates individual ports, an evaluation process was introduced determine overall success station. The proposed further evaluated binomial method. Additionally, cost procedures implemented considering maintenance configuration. findings indicate that achieving lower costs is possible through improved arrangement enhanced voltage stability.

Language: Английский

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Evaluation of Reliability and Financial Feasibility of Various EV Charging Station Layouts

Qeios, Journal Year: 2024, Volume and Issue: unknown

Published: March 25, 2024

As the popularity of electric vehicles (EVs) continues to rise, companies are increasingly focusing on expanding charging infrastructure meet growing consumer demand. Despite attempts design stations that align with distribution system requirements, ensuring reliable performance for EV ports remains a complex challenge. To address this issue, unique featuring 56 ports, comprising both uniform and non-uniform arrangements, has been introduced. These configurations underwent testing across systems ranging from 50 500 kW. Reliability assessments were carried out using established standards failure rate estimation Monte Carlo simulations evaluate port probability functions in terms reliability. By scrutinizing rates individual systematic evaluation method was gauge overall station. The proposed 56-ported station further assessed binomial method. Also, cost procedures developed, taking into account maintenance costs associated success design. research findings suggest by enhancing arrangement reliability improving voltage stability, it is possible achieve lower costs, thereby

Language: Английский

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Evaluating Reliability and Economics of EV Charging Configurations and Deep Reinforcement Learning in Robotics and Autonomy

Qeios, Journal Year: 2024, Volume and Issue: unknown

Published: April 16, 2024

Growing EV popularity drives companies to focus on reliable charging station designs despite challenges in maintaining reliability. A proposed 36-ported design combines uniform and non-uniform port arrangements, tested with 50-350 kW systems. Failure rates are estimated using MILHDBK217F MILHBK-338B standards, assessing reliability success through binomial distribution cost analysis. This improves voltage stability reduces maintenance costs enhanced In robotics autonomous systems, Deep Reinforcement Learning (DRL) excels but faces from unsafe policies leading hazardous decisions. study introduces a assessment framework for DRL-controlled formal neural network two-level verification approach evaluates safety locally reachability tools globally by aggregating local metrics across tasks. Experimental validation confirms the framework's effectiveness enhancing RAS safety.

Language: Английский

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Enhancing EV Charging Station Reliability and RAS Safety

Qeios, Journal Year: 2024, Volume and Issue: unknown

Published: May 6, 2024

The surge in electric vehicle (EV) adoption prompts companies to prioritize dependable charging station designs, despite hurdles maintaining consistency. A newly proposed design, featuring 36 ports, employs both uniform and non-uniform arrangements, subjected rigorous testing with systems ranging from 50 350 kW. Failure rates are projected through meticulous assessments based on MILHDBK217F MILHBK-338B standards, employing binomial distribution cost analysis gauge port reliability overall success rates. This innovative design not only bolsters voltage stability but also curtails maintenance expenses by bolstering reliability.In the realm of robotics autonomous (RAS), Deep Reinforcement Learning (DRL) demonstrates exceptional prowess grapples risk unsafe policies, potentially resulting perilous decisions. To address this concern, a novel study introduces evaluation framework tailored for DRL-driven systems, leveraging formal neural network analysis. adopts two-tiered verification strategy: firstly, assessing safety locally using reachability tools, secondly, aggregating local metrics across various tasks evaluate global safety. Empirical validation validates efficacy fortifying RAS.

Language: Английский

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Optimizing Vehicle-to-Vehicle Energy Sharing with Predictive Modeling

IFIP advances in information and communication technology, Journal Year: 2024, Volume and Issue: unknown, P. 300 - 313

Published: Jan. 1, 2024

Language: Английский

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Short Paper: Predicting and Analyzing EV Energy Consumption in Bangladesh : A Machine Learning Approach

Published: Dec. 19, 2024

Language: Английский

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Integrating Renewable Energy Sources into Smart Grids with an Aggregator-Based Energy Management System for Efficiency and Resilience

Published: Sept. 18, 2024

Language: Английский

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Secure and Smart: Enhancing Energy Systems in Core Electrical Networks

Published: Jan. 1, 2023

Adding new technologies to key electricity networks make them safer and more efficient. Making sure that these are reliable strong is very important as the need for energy keeps growing. This paper suggests a complete plan blends smart grid ideas with safety measures deal changing problems in systems.In order improve real-time tracking control of grid, framework stresses use devices like meters monitors. These gadgets it easier effectively, which lowers costs boosts dependability. The system also includes advanced analytics machine learning tools look at data from devices. lets repair be planned ahead time found before they happen.Secure communication methods encrypted techniques used protect sent over network. one most parts suggested system. That protects privacy, accuracy, accessibility data, instructions on how much used. Plus, has features finding reducing cyberattacks, makes overall. proposed shows update core electrical way. By using security measures, this aims systems reliable, efficient, safe. In end, will help build sustainable infrastructure.

Language: Английский

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