A player unknown's battlegrounds ranking based optimization technique for power system optimization problem

The integration of solar power with electric vehicle (EV) charging infrastructure presents a promising avenue to foster sustainable transportation. This study delves into the multifaceted challenges encountered in the synthesis of solar-powered EV charging stations and proffers solutions that span the complete energy transfer chain from photovoltaic panels to EV batteries. Initial concerns address the intermittent nature of solar energy and its impact on the reliability of power delivery. Advanced energy management strategies are explored, incorporating predictive analytics and real-time adjustment mechanisms to enhance the consistency and efficiency of power flow. The second part of the investigation scrutinizes the power electronics interface, emphasizing the need for high-efficiency converters that can operate effectively over varied solar insolation levels. Novel circuit topologies are presented, alongside adaptive control algorithms designed to optimize the power conversion process. Collectively, the findings underscore the potential of solar-powered EV charging infrastructure to not only support the decarbonization of transportation but also to contribute to the stability and efficiency of the power grid. The intersection of power electronics, energy management, and grid integration forms the cornerstone of this endeavor, with each domain providing critical components to the holistic solution. It culminates in a set of recommendations for policy, design, and operation that can accelerate the adoption of this technology.

Section: Fig 2 Predictive Analytics and Energy Management Workflowmentioning

confidence: 99%

Integration Challenges and Solutions for Solar-Powered Electric Vehicle Charging Infrastructure: From Panel to Battery

Lalitha,

Kaliyaperumal,

Nijhawan

et al. 2024

“…Machine learning algorithms assist in optimizing the UI layout, suggesting modifications based on user interaction patterns. This results in a UI that is not only tailored to the needs of differently abled users but also evolves to remain aligned with the user's behavior and preferences [26][27][28][29]. The culmination of these efforts in personalization through machine learning is a system that offers a bespoke user experience, characterized by its ability to learn, adapt, and evolve.…”

Section: Personalized User Experience Through Machine Learningmentioning

confidence: 99%

A Review on Enhancing Accessibility Through Image and Video Processing: Solutions for Differently Abled Individuals

Anjimoon,

Sobti

et al. 2024

This paper presents innovative methodologies in image and video processing aimed at augmenting accessibility for differently abled individuals. Central to this research is the development of advanced algorithms that enable enhanced interpretation and interaction with multimedia content, thereby empowering users with sensory impairments. The study introduces a multi-layered framework that integrates adaptive filtering, object recognition, and augmented reality, tailored to the needs of users with visual and auditory challenges. Semantic scene analysis is leveraged to provide descriptive audio annotations for the visually impaired, facilitating a comprehensive understanding of visual data. For individuals with hearing impairments, the system incorporates real-time sign language interpretation within videos, utilizing deep learning techniques. The efficacy of these solutions is measured against conventional accessibility tools, demonstrating significant improvements in user engagement and comprehension. A novel contribution of this research is the application of machine learning to calibrate the system according to individual user profiles, ensuring a personalized and intuitive user experience. The scalability of the proposed system is validated through its implementation across various platforms and content formats. The findings suggest that such technological advancements have the potential to significantly reduce the barriers faced by differently abled individuals in accessing multimedia information.

“…A rigorous evaluation methodology is crucial to validate the framework's capabilities in enhancing load balancing and ensuring grid stability. The assessment encompasses several key performance indicators (KPIs) such as efficiency in energy distribution, reliability in supply, peak load management, voltage regulation, and the accommodation of renewable variability [24][25][26][27][28]. To begin the evaluation, a simulation environment replicating the behavior of a typical power grid with high DER penetration is established using MATLAB SIMULINK.…”

Section: Performance Evaluationmentioning

confidence: 99%

Integration of AI in Distributed Energy Resource Management for Enhanced Load Balancing and Grid Stability

Dasari,

Raj,

Nijhawan

et al. 2024

The landscape of power systems is undergoing a transformative shift with the burgeoning inclusion of Distributed Energy Resources (DERs), which, while beneficial in enhancing the sustainability of electricity supply, introduces complexity in grid management. This paper presents a comprehensive framework leveraging Artificial Intelligence (AI) to orchestrate DER operations, thus achieving optimized load balancing and grid stability. A multi-agent system that utilizes machine learning algorithms is proposed, capable of predictive analytics and real-time decision-making. The architecture is underpinned by a robust data layer that assimilates inputs from a myriad of sensors and smart meters, facilitating the dynamic management of DERs. Through the simulation of various scenarios, the system demonstrates significant improvements in load distribution, peak shaving, and voltage regulation. The framework also showcases resilience against fluctuations and anomalies, attributing to the self-learning capability of AI models that continuously refine control strategies. The adaptability of the system is evaluated in the context of grid demand-response initiatives and the integration of intermittent renewable energy sources. Overall, the results indicate a substantial advancement in the operational efficiency of power grids, highlighting the synergy between AI and energy resource management.