Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique.

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling.

Efficient pressure regulation in nonlinear shell-and-tube steam condensers via a Novel TDn(1 + PIDn) controller and DCSA algorithm.

Steam condensers are vital components of thermal power plants, responsible for converting turbine exhaust steam back into water for reuse in the power generation cycle. Effective pressure regulation is crucial to ensure operational efficiency and equipment safety. However, conventional control strategies, such as PI, PI-PDn and FOPID controllers, often struggle to manage the nonlinearities and disturbances inherent in steam condenser systems.

Ensuring food sovereignty and nutritional sustainability in Egypt.

Understanding Egypt's dependence on wheat imports is crucial for enhancing food security and economic stability. This study aims to identify the extent of Egypt's wheat import dependency and recommend measures for increasing food self-sufficiency. We employed index analysis and an econometric model to analyze data sourced from the Food and Agriculture Organization (FAO), Observatory of Economic Complexity (OEC), Chicago Mercantile Exchange (CME), World Bank (WB), and Organisation for Economic Co-operation and Development (OECD).

Optimal design of a novel modified electric eel foraging optimization (MEEFO) based super twisting sliding mode controller for controlling the speed of a switched reluctance motor.

Switched Reluctance Motor (SRM) has a very high potential for adjustable speed drive operation due to their cost-effectiveness, high efficiency, robustness, simplicity, etc. Now a days SRMs are widely used in automotive industries as traction motors in electric vehicles and hybrid electric vehicles, air-conditioning compressors, and for other auxiliary services. In this article, a novel super twisting sliding mode controller (STSMC) is proposed to improve the performance of an SRM for reducing the ripple in speed and torque.

Comparative analysis of bulk ceramics and thick film coatings for optimized energy storage technologies.

The present investigation provides an easy and affordable strategy for fabrication of functional ceramics BiNaTiO-SrFeO (BNT-SrF5) thick films on a flexible, inexpensive and electrically integrated substrate using electrophoretic deposition process (EPD). EPD is a widely accepted, environmentally friendly method for applying coatings from a colloidal suspension to conductive substrates. Lead-free ferroelectric BNT-SrF5 powder was synthesized by solid state method to fabricate bulk samples and thick films (30-160 μm) by EPD process.

A novel artificial intelligence based multistage controller for load frequency control in power systems.

The imbalance between generated power and load demand often causes unwanted fluctuations in the frequency and tie-line power changes within a power system. To address this issue, a control process known as load frequency control (LFC) is essential. This study aims to optimize the parameters of the LFC controller for a two-area power system that includes a reheat thermal generator and a photovoltaic (PV) power plant.

Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems.

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability.

A hybrid approach for power quality event identification in power systems: Elasticnet Regression decomposition and optimized probabilistic neural networks.

The transformation of traditional grid networks towards smart-grid and microgrid concepts raises many critical issues, and quality in the power supply is one of the prominent ones that needs further research. Developing and applying power quality (PQ) recognition methods with efficient and reliable analysis are essential to the fast-growing issues related to modern smart power distribution systems. In this regard, a hybrid algorithm is proposed for PQ events detection and classification using Elasticnet Regression-based Variational Mode Decomposition (ER-VMD) and Salp Swarm Algorithm optimized Probabilistic Neural Network (SSA-PNN).

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems.

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA).

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm.

The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs.

Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability.

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC.

Correction: Hybrid optimal-FOPID based UPQC for reducing harmonics and compensate load power in renewable energy sources grid connected system.

[This corrects the article DOI: 10.1371/journal.pone.

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm.

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers.

Hybrid optimal-FOPID based UPQC for reducing harmonics and compensate load power in renewable energy sources grid connected system.

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment.

A comprehensive bibliometric survey of micro-expression recognition system based on deep learning.

Micro-expressions (ME) are rapidly occurring expressions that reveal the true emotions that a human being is trying to hide, cover, or suppress. These expressions, which reveal a person's actual feelings, have a broad spectrum of applications in public safety and clinical diagnosis. This study provides a comprehensive review of the area of ME recognition.

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control.

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus.

Optimizing grid-connected PV systems with novel super-twisting sliding mode controllers for real-time power management.

Over the past years, the use of renewable energy sources (RESs) has grown significantly as a means of providing clean energy to counteract the devastating effects of climate change. Reducing energy costs and pollution have been the primary causes of the rise in solar photovoltaic (PV) system integrations with the grid in recent years. A load that is locally connected to a GCPV requires both active and reactive power control.

Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management.

This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pumped-storage hydroelectric units. Acknowledging the variability of solar and wind energy sources and the existence of a pumped-storage hydroelectric system, this study integrates a solar-wind-thermal energy system.

Review on microgrids design and monitoring approaches for sustainable green energy networks.

Microgrids are power distribution systems that can operate either in a grid-connected configuration or in an islanded manner, depending on the availability of decentralized power resources, such as sustainable or non-sustainable power sources, battery backup systems, and power demands. The extensive adoption of inverter-based systems poses numerous technological challenges, necessitating a centralized management system to assure the system reliability and monitoring of the energy delivery networks. Thus, this research begins by highlighting these significant obstacles and then analyzes the present-day advances in multilevel control architecture for delivering on promised functionality.

Coloscopy, or colonoscopy?

In current medical and lay communication practice, the terms coloscopy and colonoscopy appear in parallel, and their usage is quite varied. The conclusion of the article is neither the form coloscopy nor the form colonoscopy is to be rejected. The final form depends on how the term got into Czech medical terminology - whether directly from Greek or via French (coloscopy) or through English (colonoscopy).

On the cardiorespiratory coordination assessed by the photoplethysmography imaging technique.

Cardiorespiratory coordination (CRC) probes the interaction between cardiac and respiratory oscillators in which cardiac and respiratory activity are synchronized, with individual heartbeats occurring at approximately the same temporal positions during several breathing cycles. An increase of CRC has previously been related to pathological stressful states. We studied CRC employing coordigrams computed from non-contact photoplethysmography imaging (PPGI) and respiratory data using the optical flow method.

Numerical Investigation of Photo-Generated Carrier Recombination Dynamics on the Device Characteristics for the Perovskite/Carbon Nitride Absorber-Layer Solar Cell.

The nitrogenated holey two-dimensional carbon nitride (C2N) has been efficaciously utilized in the fabrication of transistors, sensors, and batteries in recent years, but lacks application in the photovoltaic industry. The C2N possesses favorable optoelectronic properties. To investigate its potential feasibility for solar cells (as either an absorber layer/interface layer), we foremost detailed the numerical modeling of the double-absorber-layer−methyl ammonium lead iodide (CH3NH3PbI3) −carbon nitride (C2N) layer solar cell and subsequently provided in-depth insight into the active-layer-associated recombination losses limiting the efficiency (η) of the solar cell.

Innovative Bidirectional Isolated High-Power Density On-Board Charge for Vehicle-to-Grid.

This paper deals with developing and implementing a bidirectional galvanically isolated on-board charger of a high-power density. The power density of the new charger was 4 kW/kg and 2.46 kW/dm, and the maximum efficiency was 96.