Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems.

Adaptive modeling is imperative for analyzing nonlinear systems deployed in natural dynamic environments. It facilitates filtering, prediction, and automatic control of the target object in real time to respond to unpredictable and non-repetitive sudden physical impairment caused by ambient impacts, such as corrosion, thermal drift, interference, etc. Existing nonlinear modeling approaches, however, are too complex for online training or fall short in rapid model recalibration under such conditions.

Enhanced fruit preservation using compressed stabilized earth blocks and municipal solid waste incinerator bottom ash in pyramid structures.

Storage conditions play a crucial role in preserving fruit quality, regulating ripening, and preventing degradation. The current research examines the impact of fruit quality stored in controlled environment where compressed stabilised earth blocks (CSEBs) were produced incorporating municipal solid waste incinerator bottom ash (MSWIBA). To improve fruit storage efficiency, this research examines the impact of incorporating municipal solid waste incinerator bottom ash (MSWIBA) into compressed stabilised earth blocks (CSEBs).

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous monitoring and early detection are necessary for effective management. The Metaverse Environment (ME) has gained attention in the digital healthcare environment; yet, it lacks adequate support for disabled individuals, including deaf and dumb people, and also faces challenges in security, generalizability, and feature selection. To overcome these limitations, a novel probabilistic-centric optimized recurrent sechelliott neural network (PO-RSNN)-based diabetes prediction (DP) and Fuzzy Z-log-clipping inference system (FZCIS)-based severity level estimation in ME is carried out.

Energy efficiency evaluation of construction projects using data envelopment analysis and Tobit regression.

Energy efficiency (EE) in the construction sector is crucial for sustainable development, particularly in emerging economies like Pakistan, where the industry accounts for a large share of energy consumption and environmental degradation. Despite its economic significance, Pakistan's construction sector suffers from inefficiencies in energy use, with limited comprehensive assessments to guide improvements. This research introduces a novel, integrated approach combining Data Envelopment Analysis (DEA) and Tobit regression to evaluate and enhance EE in construction projects.

A novel network lifetime maximization technique in WSN using energy efficient algorithms.

Recent advances in wireless communication have enabled the development of small, low-cost, wearable sensors, which play a crucial role in applications such as healthcare monitoring, environmental sensing, and industrial automation. However, maximizing network lifetime (NL) and optimizing energy consumption remain key challenges in Wireless Sensor Networks (WSNs). Existing routing algorithms often struggle to balance energy efficiency and service quality, leading to premature network failures.

Cognitive fuzzy logic-integrated energy management for self-sustaining hybrid renewable microgrids.

The Sustainable Energy Resource integrated with Energy Storage System is deployed inside a microgrid, using a power management method to effectively regulate energy consumption during peak demand. Demand-based energy management measures, such as distributing load and stalling appliance usage amid peak hours are executed. An Integrated Energy Management System (EMS) was proposed employing fuzzy logic as a solution to manage the energy needs of loads in this work.

Hierarchical multi step Gray Wolf optimization algorithm for energy systems optimization.

Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems in various domains, including engineering design, image processing, and machine learning. However, standard GWO can suffer from premature convergence and sensitivity to parameter settings. To address these limitations, this paper introduces the Hierarchical Multi-Step Gray Wolf Optimization (HMS-GWO) algorithm.

An enhanced protection scheme for power transformers integrating alpha plane analysis.

Power transformers play a critical role in electric power systems by facilitating connections between subsystems with different voltage levels. Ensuring their reliable operation and protection against faults is essential for maintaining network stability. Percentage differential (PD) protection, commonly used in transformer protection relays, faces limitations such as dealing with current transformer (CT) saturation during external faults and difficulty in detecting certain fault types.

Enhancing energy efficiency in buildings using sawdust-based insulation in hot arid climates.

In the quest for sustainable construction solutions, this study explores the thermal insulation potential of sawdust as an eco-friendly material for building applications in hot-arid climates, with a focus on Iraq. The research evaluates the thermal behavior of sawdust when mixed with clay and glue, forming two different composite insulation materials. Laboratory experiments were conducted to measure thermal conductivity, with results compared against traditional insulators like Styrofoam.

Optimized FOPID controller for steam condenser system in power plants using the sinh-cosh optimizer.

Steam condensers in power plants are crucial for improving the efficiency of the power generation cycle by condensing and recycling steam from the turbine. We used fractional-order proportional-integral-derivative (FOPID) controller to regulate the pressure inside the steam condenser system. We adopted sinh-cosh optimizer (SCHO) to tune this controller.

Hybrid machine learning framework for predictive maintenance and anomaly detection in lithium-ion batteries using enhanced random forest.

The critical necessity for sophisticated predictive maintenance solutions to optimize performance and extend lifespan is underscored by the widespread adoption of lithium-ion batteries across industries, including electric vehicles and energy storage systems. This study introduces a comprehensive predictive maintenance framework that incorporates real-time health diagnostics with state-of-charge (SOC) estimation, utilizing an Improved Random Forest (IRF) algorithm to address the current limitations in battery management systems. The framework integrates physics-informed methodologies with data-driven machine learning models to facilitate the dynamic assessment of battery health and the production of precise predictions.

Optimizing sustainable energy management in grid connected microgrids using quantum particle swarm optimization for cost and emission reduction.

The global shift towards decentralized energy systems, driven by the integration of distributed generation technologies and renewable energy sources, underscores the critical need for effective energy management strategies in microgrids. This study proposes a novel multi-objective optimization framework for grid-connected microgrids using quantum particle swarm optimization (QPSO) to address the dual challenges of minimizing operational costs and reducing environmental emissions. The microgrid configuration analyzed includes renewable energy sources like photovoltaic panels and wind turbines, along with conventional energy sources and battery storage.

Advanced AI-driven techniques for fault and transient analysis in high-voltage power systems.

Each substation is critically essential to the overall operation of the electrical power system. Potential dangers include thermal stress, noise, slip, trip, fall hazards, animal waste, and nonionizing radiation. These are the causes of joint failures of cables and overhead lines, failure of one or more phases of circuit breakers, and melting of fuses or conductors in one or more phases.

Optimal scheduling of solar powered EV charging stations in a radial distribution system using opposition-based competitive swarm optimization.

Solar-powered EV charging stations offer a sustainable and reliable alternative to traditional charging infrastructure, significantly alleviating stress on legacy grid systems. However, the intermittent nature of renewable energy sources poses a challenge for energy management in power distribution networks. To address this, optimal charge/discharge scheduling of EVs becomes crucial.

Comparative analysis of brushless DC and switched reluctance motors for optimizing off-grid water pumping.

Off-grid water pumping systems (OGWPS) have become an increasingly popular area of research in the search for sustainable energy solutions. This paper presents a finite element method (FEM)-based design and analysis of Brushless-DC (BLDC) and Switched Reluctance Motors (SRM) designed for low-power water pumping applications. Utilizing adaptive finite element analysis (FEA), both motors were designed with identical ratings and design parameters to ensure a fair comparison.

Robust fault detection and classification in power transmission lines via ensemble machine learning models.

Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms-including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks-are evaluated.

Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency.

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method-Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models-exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)-are developed, each reflecting a different price elasticity of demand.

Energy feature extraction and visualization of voltage sags using wavelet packet analysis for enhanced power quality monitoring.

Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring.

Wind energy assessment and hybrid micro-grid optimization for selected regions of Saudi Arabia.

This study investigates the optimization of wind energy integration in hybrid micro grids (MGs) to address the rising demand for renewable energy, particularly in regions with limited wind potential. A comprehensive assessment of wind energy potential was conducted, and optimal sizing of standalone MGs incorporating photovoltaic (PV) systems, wind turbines (WT), and battery storage (BS) systems was performed for six regions in the Kingdom Saudi Arabia. Wind resource analysis utilizing the Weibull distribution function shows that all regions exhibited Class 1 wind energy characteristics, with average annual wind power densities ranging from 36.

An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods.

This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent in interconnected power systems, leading to slower settling time and higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM's capacity to learn from historical data by using gradient descent to forecast the future disturbances, while the GA optimizes the PID parameters in real time, ensuring dynamic adaptability and improved control precision.

Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation.

Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance of these systems. PV power plants operating under fault conditions show significant deviations in current-voltage (I-V) characteristics compared to those under normal conditions.

Efficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization.

Electric furnaces play an important role in many industrial processes where precise temperature control is essential to ensure production efficiency and product quality. Traditional proportional-integral-derivative (PID) controllers and their modified versions are commonly used to maintain temperature stability by reacting quickly to deviations. In this study, the real PID plus second-order derivative (RPIDD) controller is introduced for the first time for industrial temperature control applications, which is a novel alternative that has not yet been investigated in the literature.

Coordinated charging of EV fleets in community parking lots to maximize benefits using a three-stage energy management system.

The rapid global adoption of electric vehicles (EVs) necessitates the development of advanced EV charging infrastructure to meet rising energy demands. In particular, community parking lots (CPLs) offer significant opportunities for coordinating EVs' charging. By integrating energy storage systems (ESSs), renewable energy sources (RESs), and building prosumers, substantial reductions in peak load and electricity costs can be achieved, while simultaneously promoting environmental sustainability.

Development of a high-performance pseudocapacitive composite via electroless deposition of silver nanoparticles on micro-sized silicon.

An energy material has been developed using a one-step chemical reduction method, incorporating silver nanoparticles (AgNPs) that encapsulate micro-sized silicon (mSi) flakes. SEM investigation revealed complete encapsulation of silicon flakes by AgNP's dendritic structure, EDX confirmed the deposition of Ag on Si flakes. Raman spectroscopy confirmed the formation of silver and silicon oxides.