Power and Energy Commons^™

Streamer Discharge Modeling For Plasma-Assisted Combustion, Stuart Reyes, Shirshak Kumar Dhali

Electrical & Computer Engineering Faculty Publications

Some of the popular and successful atmospheric pressure fuel/air plasma-assisted combustion methods use repetitive ns pulsed discharges and dielectric-barrier discharges. The transient phase in such discharges is dominated by transport under strong space charge from ionization fronts, which is best characterized by the streamer model. The role of the nonthermal plasma in such discharges is to produce radicals, which accelerates the chemical conversion reaction leading to temperature rise and ignition. Therefore, the characterization of the streamer and its energy partitioning is essential to develop a predictive model. We examine the important characteristics of streamers that influence combustion and develop some …

Distributed Energy Resources (Der) Capacity Estimation With Spatiotemporal Downscaling For Transmission And Distribution Coordination, Abhilasha Suvedi

Electronic Theses and Dissertations

The primary objective of this thesis is to accurately estimate the capacity of distributed energy resources (DERs) using the spatiotemporally downscaled local solar irradiance for their efficient integration into transmission-level grid operations. Accurate estimation of DER capacity is crucial for their enhanced integration into real-time electricity markets. Different methods of spatiotemporally downscaling the solar irradiance are presented in this thesis, followed by their integration into the 12 house distribution network – a low voltage residential distribution system. Grid support functions (GSFs) like Volt-Watt Control, Volt-VAR control, and Frequency-Watt Control are applied to effectively regulate the frequency and maintain the voltage …

Solar Energy Prediction Using Advanced Hybrid Machine Learning Models, Jelawi A. Alqhtani

UNF Graduate Theses and Dissertations

Accurate short-term forecasting of solar power generation is critical for the reliable and cost-effective operation of renewable-based microgrids, where sudden weather-induced variability can compromise grid stability, battery scheduling, and energy trading decisions. Traditional physical and statistical models struggle to capture the complex non-linear relationships and localized weather effects, while individual deep learning architectures often exhibit systematic biases such as chronic under-prediction of peak generation. This thesis proposes a novel Cross-Feedback Ensemble framework that combines the complementary strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1D-CNN) models through an iterative cross-feedback mechanism and a …

Flame Boundary Effects Of Hydrogen-Air Premixing, Alecson L. De Lima Junior

Honors Undergraduate Theses

The objective of this research was to investigate the reaction of hydrogen and heated air premixing when injected into a combusting ethylene-air crossflow, and to study the flame stabilization location with varying premixing levels. The main parameter of this investigation was the flame stabilization diagnosed through chemiluminescence, based on recorded equivalence ratio, temperature, and flame boundaries, and maintaining a constant air temperature, hydrogen and air mass flow, and momentum flux ratios. Testing was conducted at different ratios of hydrogen and air premixing achieved through alternating the distance between the point where hydrogen and air are mixed and the combusting crossflow. …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

A Proximal Policy Optimization-Based Controller For Enhanced Power Sharing In Microgrids, Seyedmohammad Hasheminasab, Armin Lotfy, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

This paper introduces a Proximal Policy Optimization (PPO)-based virtual impedance (VI) controller to enhance both power sharing and system response under disturbances in inverter-interfaced microgrids. Traditional droop control methods often face challenges due to variations in feeder impedance, which degrade performance. The proposed controller continuously updates its policy based on changes in the operating environment. The control problem is modeled as a Markov Decision Process (MDP), in which the state and action spaces are explicitly defined, and a carefully designed reward function, satisfying system criteria and constraints, guides the learning process toward achieving the desired transient and steady-state performance. By …

Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Snake Optimization Algorithm For Fractional Order Controller In Three-Area Load Frequency Regulation, Shubra Goel, Omveer Singh

ASEAN Journal on Science and Technology for Development

The transition toward renewable energy-dominated power systems have accentuated the intricacies of frequency control, necessitating advanced regulatory mechanisms. This investigation articulates a tri-zonal frequency stabilization approach, employing a hybridized controller that synergizes Fuzzy Fractional-Order PI and Tilt-Integral-Derivative methodologies. The uniqueness of this approach is further amplified by the deployment of the Snake Optimization algorithm for precise parameter tuning. Set within a conventional grid topology integrated with assorted renewable energy sources, the study evaluates the controller’s adaptability and resilience through a series of comprehensive scenario-based analyses.

Determination Of Techno-Economic Indicators Of Pumped Storage Power Plants Under The Operational Conditions Of The Tuyamuyun Hydro Complex, Kurbon Salikhdjanovich Dzhuraev, Abdurauf Abduaziz Uulu, Fotima Tulkunovna Shadibekova

Technical science and innovation

The article presents the results of research on the analysis of the state of the electric power system of the Republic of Uzbekistan, taking into account the operating modes of hydraulic and thermal power plants with the study of the sharp-peak nature of the electric load in the winter and summer periods of the year and the problems of covering daily load schedules due to a lack of maneuverable capacities. The use of pumped storage power plants is proposed to cover and smooth daily load schedules across months and seasons. The necessity of utilizing the capacity of pumped storage power …

Method For Calculating Characteristic Frequency Ab (Rechargeable Batteries), Alisher Ganisher Ugli Bozorov

Technical science and innovation

A method for calculating the characteristic frequency is proposed, based on automatic interpolation of the frequency dependence of the reactance and subsequent calculation of the characteristic frequency as one of the roots of the interpolation function. The developed algorithm allows you to calculate the characteristic frequency from only 4 points of the frequency dependence of the reactance. In this case, the error in calculating the resonant frequency parameter does not exceed ~2 Hz.

Obviously, the values of the coefficients of the equation depend on the number of resistance values X and the corresponding frequencies of the test signal. The indicated …

Dendrite-Free Strategies For Aqueous Zinc-Ion Batteries: Structure, Electrolyte, And Separator, Gang Wu, Wu-Hai Yang, Yang Yang, Hui-Jun Yang

Journal of Electrochemistry

Continued growth in energy demand and increased environmental pollution constitute major challenges that need to be addressed urgently. The development and utilization of renewable, sustainable, and clean energy sources, such as wind and solar, are crucial. However, the instability of these intermittent energy sources makes the need for energy storage systems increasingly urgent. Aqueous zinc-ion batteries (AZIBs) have received widespread attention due to their unique advantages, such as high energy density, cost-effectiveness, environmental friendliness, and safety. However, AZIBs face significant challenges, mainly the formation of zinc dendrites that seriously affect the stability and lifetime of the batteries, leading to battery …

All-Solid-State Sodium-Ion Batteries: A Leading Contender In The Next-Generation Battery Race, Rui-Jie Zhu, Ze-Chen Li, Wei Zhang, Akira Nasu, Hiroaki Kobayashi, Masaki Matsui

Journal of Electrochemistry

All-solid-state lithium-ion batteries (LIBs) using ceramic electrolytes are considered the ideal form of rechargeable batteries due to their high energy density and safety. However, in the pursuit of all-solid-state LIBs, the issue of lithium resource availability is selectively overlooked. Considering that the amount of lithium required for all-solid-state LIBs is not sustainable with current lithium resources, another system that also offers the dual advantages of high energy density and safety— all-solid-state sodium-ion batteries (SIBs) —holds significant sustainable advantages and is likely to be the strong contender in the competition for developing next-generation high-energy-density batteries. This article briefly introduces the research …

Contributions To Journal Of Electrochemistry, The Flagship Journal Of Chinese Chemical Society Electrochemical Committee, Will Be Listed As A New Recommendation Policy For China Youth Awards Of Electrochemist, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Electrochemical-Method-Induced Strong Metal-Support Interaction In Pt-Cnt@Sno2 For Co-Tolerant Hydrogen Oxidation Reaction, Shen-Zhou Li, Zi-Jie Lin, Qi-An Chen, Zhao Cai, Qing Li

Journal of Electrochemistry

Inducing the classic strong metal-support interaction (SMSI) is an effective approach to enhance the performance of supported metal catalysts by encapsulating the metal nanoparticles (NPs) with supports. Conventional thermal reduction method for inducing SMSI processes is often accompanied by undesirable structural evolution of metal NPs. In this study, a mild electrochemical method has been developed as a new approach to induce SMSI, using the cable structured core@shell CNT@SnO₂ loaded Pt NPs as a proof of concept. The induced SnO_x encapsulation layer on the surface of Pt NPs can protect Pt NPs from the poisoned of CO impurity in …

Design, Control, And Evaluation Of A Photovoltaic Snow Removal Strategy Based On A Bidirectional Dc-Dc Converter For Photovoltaic–Electric Vehicle Application, Salma Elakkad, Mohamed Hesham, Hany Ayad Bastawrous, Peter Makeen

Electrical Engineering

A novel self-heating technique is proposed to clear snow from photovoltaic panels as a solution to the issue of winter snow accumulation in photovoltaic (PV) power plants. This approach aims to address the shortcomings of existing methods. It reduces PV cell wear, resource loss, and safety risks, without the need for additional devices. A self-heating current is applied to the solar panel to melt the snow covering its surface, which is then allowed to slide off the panel due to gravity. The proposed system consists of a bidirectional DC-DC converter, which removes the snow cover by heating the solar PV …

Leveraging P4 Programmable-Hardware Switches For In-Network Pmu Packet Recovery, Evan Michael Bonar

Electrical Engineering and Computer Science Undergraduate Honors Theses

Phasor Measurement Unit (PMU) systems are essential for real-time power grid monitor- ing but often face data loss due to network delays, equipment malfunctions, or transmis- sion errors. Traditional centralized recovery solutions introduce significant latency and scalability challenges. This thesis presents a P4-based in-network recovery mechanism that embeds detection and recovery directly into the data plane of P4-enabled programmable switches, significantly reducing recovery time and infrastructure complexity. Using the Aurora 610 switch, the system detects missing packets via sequence number analysis and recovers magnitudes with an efficient register-based algorithm.

Evaluation demonstrates high accuracy and low latency, achieving a mean absolute …

Artificial Intelligence Computational Techniques Of Flywheel Energy Storage Systems Integrated With Green Energy: A Comprehensive Review, Abdelmonem Draz, Hossam Ashraf, Peter Makeen

Electrical Engineering

In recent years, the operation of the electric power grid has become more efficient and resilient due to the integration of renewable energy sources (RESs). Solar and wind energy are being incorporated aggressively into the main grid, while other RESs like biomass and geothermal energy are also on the rise. However, the intermittent nature of these RESs necessitates the use of energy storage devices (ESDs) as a backup for electricity generation such as batteries, supercapacitors, and flywheel energy storage systems (FESS). This paper provides a thorough review of the standardization, market applications, and grid integration of FESS. It examines the …

Building Energy Management: A Data-Driven Approach Using Clustering And Load Forecasting, Aviral Kandel

LSU New Orleans Theses and Dissertations

The increase of smart meters in the grid has led to the generation of a vast amount of high dimensional energy data with improving temporal resolution. During analysis, relying on short samples like a day or week of data, could lead to wrong conclusion due to seasonal dynamics and customer behavior variations. To effectively utilize the vast amount of information, it must be compressed into a low-dimensional representation. This thesis explores the state-of-the-art dimensionality reduction techniques for a high-dimensional, non-linear energy dataset and proposes a novel deep learning based method to address the limitation of existing approaches. The proposed method …

Reference Dependence In Queue Design And Pricing Strategies, Jian Liu, Yongpin Zhou, Jian Chen, Peng Li

Electrical and Computer Engineering Faculty Research & Creative Works

This research investigates the effect of reference dependence on waiting times in service systems which formerly used a first-in-first-out (FIFO) service but have introduced a priority line with a fee. Our model combines reference-dependent gain-loss utility with standard customer utility, and we posit that customers are pleased with shorter-than-expected waiting times, whereas longer-than-expected times lead to dissatisfaction and an increased likelihood of balking. The study explores two scenarios: a captive customer system (CCS) and a noncaptive customer system (NCCS), with a focus on optimal pricing and segmentation strategies for revenue and social welfare maximization. The results reveal that, in a …

Hardware Applications Of High-Speed Fault Detection Algorithms, Daniel Zintsmaster

All Theses

The growing need for a sustainable electric energy infrastructure has driven research into control, protection, and optimization of power systems. A key challenge is that the changing grid must operate at increasingly higher speeds, but many current hardware devices cannot meet these demands. This thesis focuses on power system protection, aiming to design a hardware solution that can detect and isolate faults in microseconds, ensuring faster, more reliable grid operations. The first hardware developed was for a low-voltage direct current (LVdc) microgrid, which faces challenges due to a lack of protection schemes and novel speed requirements. This thesis presents protection …

Short-Circuit Modeling And Instantaneous Overvoltage Protection Of Renewable Farms, Shah Mohazzem Hossain

All Dissertations

The existing utility grid is experiencing a paradigm shift in electricity generation due to a significant penetration of renewable energy sources. This shift is primarily driven by the large-scale integration of solar and wind energy farms, particularly in the transmission network, where these farms are typically accompanied by battery energy storage in a hybrid configuration to overcome source intermittency and improve reliability. Such renewable farms comprise numerous inverters, pad-mounted transformers, collector feeders, and dc-side components depending on the generation-source type. Additionally, inverters within these farms exhibit distinct operating modes, such as `grid-following'(GFL) and `grid-forming'(GFM), as well as fault control modes, …

Aqueous Supercapacitors With Wide Operative Voltage Window And Long Cycling Life Enabled By Electrolyte Hybridization Effect, Shi-Hua Ma, Qi Yin, Jin-Ping Zhao

Journal of Electrochemistry

Developing supercapacitors (SCs) with long cycling life and wide operative voltage window is a significant topic in the field of aqueous electrolytes. Although the design of water in salt (WIS) electrolytes has pushed the development of aqueous electrolytes to a new height, the WIS electrolytes with an operative voltage window of up to 2.5 V is still very scarce. Herein, in order to enrich the type of aqueous electrolyte with high operative voltage, tetramethylammonium trifluoromethanesulfonate (TMAOTf) based WIS electrolyte was used as a model to construct WIS based hybrid electrolyte with acetonitrile (ACN) co-solvent and LiTFSI co-solute. In view of …

Journal Of Electrochemistry, The Flagship Journal Of Chinese Chemical Society Electrochemical Committee, Made A Public Appearance At The 22nd National Electrochemical Congress, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

The Award Ceremony Of The 2nd Xiamen University Short Video Abstract Contest For Science And Technology Journal Papers Came To A Successful Conclusion, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

The 9th National Conference Of Solid State Battery Successfully Held In Xiamen, Fujian, Zheng-Liang Gong

Journal of Electrochemistry

No abstract provided.

Automated Solutions For Hydroponic Plant Growth, Sydney Mcclure, Adam Lachguar

Sustainability Conference

Within the past year, Project H.O.M.E. has been focusing on the design and development of a semi-automatic hydroponic system specifically for sustaining plant life in Martian-like conditions. Given the significance of extended space-based travel, where the duration of human life in space is a crucial factor, growing food becomes imperative. This project has integrated electrical engineering and computer science, with features like automated pH testing and sensor-based evaluations. Key functionalities, including timed watering and automatic adjustments, were coded to enhance plant care. Initially, the project’s comprehensive research and strategic planning resulted in detailed blueprints and computer-aided design models for the …

Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr

Articles

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …