Real-time Validation Research Articles

Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation

Assessing Stability in Renewable Microgrid Using a Novel-Optimized Controller for PVBattery Based Micro Grid with Opal-RT-Based Real-Time Validation

Comparative study of improved control strategies for DFIG in wind energy conversion systems: A dSpace real-time implementation approach

This research paper focuses on the comparison of two distinct strategies for direct power control (DPC) of a doubly fed induction generator (DFIG) in wind energy conversion systems (WECSs). The study places particular emphasis on algorithms based on low-pass filters for the estimation of stator and rotor flux, which have demonstrated good performance characteristics. Detailed theoretical analysis and implementation procedures of the proposed methods are presented. To evaluate and compare the performance of the control strategies, MATLAB/Simulink software is utilized for comprehensive analysis. The obtained results, both quantitative and qualitative, from this comparative analysis are expected to be of great interest to researchers engaged in the field of DFIG-based WECSs. Furthermore, real-time validation is conducted to demonstrate the effectiveness of the proposed control strategy, specifically focusing on the DPC approach based on stator flux estimation. A dSpace 1104 real-time card is employed and tested with a wind turbine emulator. The experimental results obtained through this validation process provide evidence of the efficiency and validity of the proposed control strategy. Overall, this research contributes to the advancement of DPC strategies for DFIG-based WECS by comparing and evaluating different control methods, providing in-depth theoretical analysis, and validating the proposed approach through real-time experimentation. The findings affirm the efficiency and effectiveness of the proposed control strategy, further establishing its value in practical applications.

Characterization and expression analysis of the MADS-box gene AGL8 in cotton: insights into gene function differentiation in plant growth and stress resistance.

AGAMOUS-LIKE 8 (AGL8) belongs to the MADS-box family, which plays important roles in transcriptional regulation, sequence-specific DNA binding and other biological processes and molecular functions. The genome of cotton, a representative polyploid plant, contains multiple AGL8 genes. However, their functional differentiation is still unclear. In this study, a comprehensive genomic analysis of AGL8 genes was conducted. Cotton AGL8s were subdivided into four subgroups (Groups 1, 2, 3, and 4) based on phylogenetic analysis, and different subgroups of AGL8s presented different characteristics, including different structures and conserved motifs. With respect to the promoter regions of the GhAGL8 genes, we successfully predicted cis-elements that respond to phytohormone signal transduction and the stress response of plants. Transcriptome data and real-time quantitative PCR validation indicated that three genes, namely, GH_D07G0744, GH_A03G0856 and GH_A07G0749, were highly induced by methyl jasmonate (MeJA), salicylic acid (SA), and abscisic acid (ABA), which indicated that they function in plant resistance to abiotic and biotic stresses. The information from the gene structure, number and types of conserved domains, tissue-specific expression levels, and expression patterns under different treatments highlights the differences in sequence and function of the cotton AGL8 genes. Different AGL8s play roles in vegetative growth, reproductive development, and plant stress resistance. These results lay a foundation for further study of GhAGL8s in cotton.

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

Transcriptome Sequencing of Gingival Tissues from Impacted Third Molars Patients Reveals the Alterations of Gene Expression.

The removal of impacted third molars by surgery may occur with a series of complications, whereas limited information about the postoperative pathogenesis is available. The objective of this study is to identify changes in gene expression after flap surgical removal of impacted third molars and provide potential information to reduce postoperative complications. The gingival tissues of twenty patients with flap surgical removal of impacted third molars and twenty healthy volunteers were collected for gene expression testing. The collected gingival tissues were used RNA sequencing technology and quantitative real-time PCR validation was performed. DEG was mapped to protein databases such as GO and KEGG for functional annotation and, based on annotation information, for mining of differential expression genes in patients with mpacted third molars. A total of 555 genes were differentially expressed. Among the top up-regulated genes, HLA-DRB4, CCL20, and CXCL8 were strongly associated with immune response and signal transduction. Among the top down-regulated genes, SPRR2B, CLDN17, LCE3D and LCE3E were related to keratinocyte differentiation, IFITM5, and BGLAP were related to bone mineralization, UGT2B17 is associated with susceptibility to osteoporosis. KEGG results showed that the DEGs were related to multiple disease-related pathways. This first transcriptome analysis of gingival tissues from patients with surgical removal of impacted third molars provides new insights into postoperative genetic changes. The results may establish a basis for future research on minimizing the incidence of complications after flap-treated third molars.

Data Models for Casting Processes – Performances, Validations and Challenges

Abstract Data-driven models with their associated data learning and training schemes can be utilised for the light metal casting processes. This paper presents the basis of data model building processes along with data training and learning exercises for vertical direct chill casting and high pressure die casting (HPDC) applications. The concepts of efficient database building, data translations and sampling, as well as real-time model building and validations are briefly discussed. Rigorous performance studies were additionally carried out for two real-world case studies. Different combinations of data solvers and interpolators are adapted for the model building techniques, while machine learning schemes are used for data trainings.

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

Genome-Wide Identification of WRKY Transcription Factors in Pea (Pisum sativum L.) and their Response to Sulfur and Water Stress

AbstractThe WRKY gene family is widely distributed in plants and plays essential roles in plant development and stress response. Despite extensive characterization of WRKY transcription factors in many plants, a genome-wide analysis of the WRKY gene family in Pisum sativum is still lacking. The central method of this study focused on the retrieval and characterization of WRKY gene sequences in the pea genome and further looking up for their modulation in response to sulfur and water stress. This involved a combination of bioinformatics tools and gene expression study to identify, characterize, and understand the properties of the WRKY gene family in pea. In this study, 86 PsWRKY genes were identified in the pea genome, categorized into five phylogenetic groups. They were dispersed across all seven chromosomes, with Chromosome 5 showing the highest enrichment. Synteny analysis revealed orthologs of 69 PsWRKY genes in Arabidopsis and Medicago. Transcriptome analysis identified 36 differentially expressed PsWRKY genes in response to sulfur (S) stress, water (W) stress, and their combination (W + S). Real-time PCR validation confirmed significant upregulation of PsWRKY23, PsWRKY58, PsWRKY64, and PsWRKY83 under water stress, PsWRKY58 and PsWRKY84 under sulfur stress, and PsWRKY03 under combined (W + S) stress. Overall, this study provides important insights on genetic information of the WRKY gene family in Pea and its possible role in water and sulfur stress, which will help further to study these candidate WRKY gene with a future objective of understanding their role in abiotic stress.

Evaluating the impact of human movement-induced airflow on particle dispersion: A novel real-time validation using IoT technology

Human movement significantly influences local airflow and particle dispersion in indoor environments. Therefore, this study integrates the dynamics of human walking into the analysis of airborne infection risks in a positive pressure isolation ward designed for the protection of immunocompromised patients. Real-time data collection was performed using an anemometer and IoT-based PM sensors to measure airflow velocities and particulate matter (PM) concentrations within a full-scale experimental chamber. Computational Fluid Dynamics (CFD) was used for the numerical simulations, and a User-Defined Function (UDF) code simulated the translational movement of a manikin. The results demonstrated strong agreement with the experimental data, thereby validating the airflow turbulence and Lagrangian-based Discrete Phase Model (DPM) in predicting the airflow velocities and particle transport. The study revealed that human walking substantially enhanced particle dispersion distance by 10-fold compared to static conditions, primarily attributed to intensified air mixing induced by the body movement. Furthermore, the CFD analysis underscored that the direction of walking plays a crucial role in airborne transmission. Specifically, walking away from a patient did not elevate infection risk, whereas approaching a patient significantly increased particle deposition in the patient-occupied region. This study highlights the critical need to consider both movement patterns and directional flow in managing airborne infection risks, contributing to the development of more effective infection control strategies in healthcare settings.

Genomic Insights into Disease Resistance in Sunflower (Helianthus annuus): Identifying Key Regions and Candidate Genes for Verticillium dahliae Resistance.

Sunflower (Helianthus annuus) is a globally significant field crop, and disease resistance is crucial for ensuring yield stability and crop quality. Verticillium dahliae is a notorious soilborne pathogen that causes Verticillium Wilt (VW) and threatens sunflower production worldwide. In this study, we conducted a comprehensive assessment of sunflower resistance to V. dahliae across 231 sunflower cultivar lines, from the Sunflower Association Mapping (SAM) population. We employed EMMAX and ridge regression best linear unbiased prediction (rrBLUP) and identified 148 quantitative trait loci (QTLs) and 23 putative genes associated with V. dahliae resistance, including receptor like kinases, cell wall modification, transcriptional regulation, plant stress signalling and defense regulation genes. Our enrichment and quantitative real-time PCR validation results highlight the importance of membrane vesicle trafficking in the sunflower immune system for efficient signaling and defense upon activation by V. dahliae. This study also reveals the polygenic architecture of V. dahliae resistance in sunflowers and provides insights for breeding sunflower cultivars resistant to VW. This research contributes to ongoing efforts to enhance crop resilience and reduce yield losses due to VW, ultimately benefiting sunflower growers and the agricultural sector.

Deciphering anhydrobiosis and plant parasitism of the wheat seed gall nematode, Anguina tritici through transcriptomic analysis

The wheat seed gall nematode (Anguina tritici L.) poses a significant threat to wheat and barley production. Central to its survival is the ability to endure desiccation through anhydrobiosis, setting it apart from other nematodes. Unraveling the molecular mechanisms governing anhydrobiosis and plant parasitism in A. tritici is crucial for devising effective management strategies. A comprehensive transcriptomic analysis was conducted to elucidate the genetic pathways involved in these processes. Employing next-generation sequencing technologies, gene expression profiling was performed across three developmental stages, namely anhydrobiotic J2, revived J2 and the adult. Transcriptomic profiling yielded 139,002 transcripts in anhydrobiotic J2s, 156,731 in revived J2s, and 80,282 in adult stages, with a cumulative assembly size of 133.2 Mb. Differential gene expression analysis revealed that 1223 genes were significantly upregulated whereas 1934 genes were downregulated in anhydrobiotic J2s in comparison to revived J2s. Comparative transcriptomic analysis across nematode species highlighted conserved and species-specific genes. Functional annotation identified key genes associated with anhydrobiosis, plant parasitism, glycosylation, and longevity mechanisms. Real-time PCR validation corroborated differential expression patterns. The present study provides gene homologs and longevity pathways, which indicate similarity of the mechanism of dauer larva formation and survival strategies to that of Caenorhabditis elegans. Comprehensive understanding of the molecular basis for survival and parasitic strategies of A. tritici, shall provide potential targets for novel control interventions to mitigate nematode's impact on wheat crops.

Experimental Implementation of a TD3 Agent Based Speed Controller for Direct Torque Control of PMSM Drives

This manuscript presents a novel control approach for Permanent Magnet Synchronous Motors (PMSMs) by integrating the widely used Direct Torque Control (DTC) method with Deep Reinforcement Learning (DRL), specifically the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The TD3 algorithm is well-suited to address the challenges posed by high-dimensional state and action spaces in PMSM drives. The conventional Proportional–Integral (PI) controller in the outer loop of DTC is replaced with the DRL based TD3 agent to overcome the limitations of traditional PI controllers, such as model dependency and complex parameter tuning. The DRL technique allows the agent to learn optimal control policies from experience, making it suitable for complex and nonlinear control problems. Extensive simulation studies demonstrate the effectiveness of the TD3-based control in achieving accurate speed tracking under varying operating conditions. Real-time experimental validation using a TMS320F28379D digital signal processor confirms the feasibility of the proposed DRL based approach. The research offers new insights into improving the performance of PMSM drives and paves the way for future advancements in electric drive control.

Formulating iron ore pellet induration process in an industrial straight grate system: Real-time experimentation and validation

Understanding the complex process of iron ore pellet induration is crucial in a straight grate system in the steelmaking industry. There is no attempt made so far to predict the behavior of this unit considering the physiochemical processes (drying, carbon combustion and limestone calcination) along with the heat transfer within the pellet, and between the hot gas and pellet. Moreover, the reported models are validated mainly with the pot test data. To address these research gaps, in this contribution, a rigorous model framework is proposed for an industrial induration unit considering all these above-mentioned issues together with some other practical aspects, including the heat transfer from hot gas to grate bar and heat loss due to air leakage into the system. To validate this rigorous formulation, attempt is further made to perform the real-time experimentation with induration system equipped with a thermocar. It is shown first time that the predicted temperature profiles of the pellets, grate bar and exit gas associated with the running bed are consistent with the plant data. The fuel consumption data is further obtained to investigate the performance of the proposed formulation at different operating regimes. It is observed that the mean absolute percentage error (MAPE) between the measured and predicted fuel rates is reasonably low (i.e., 4.2%). With this, it is recommended to use the proposed formulation for online property prediction, process design, optimization, troubleshooting, control and scale-up of the induration unit.

Angle Accuracy of Intramedullary Bone Resection Guides for Total Knee Arthroplasty.

The importance of proper prosthetic placement has been confirmed in numerous studies. The objective of this study was to compare the planned resection angles to the verified intraoperative angles of femoral and tibial varus/valgus, tibial slope, and femoral flexion for each total knee performed using intramedullary (IM) cut guides for both distal femur and proximal tibia cuts. A total of 1,000 total knee arthroplasties (TKAs) were evaluated for this study. Intraoperative cut-check technology was used to show real-time validation of these resection angles. Assuming an acceptable range of within 2° of the planned cuts, results show the femoral varus/valgus angles were 75% accurate, the femoral flexion angles were 50.8% accurate, the tibial cuts were 95.2% accurate in the coronal plane, and the tibial slope was the least accurate with only 50.3% within the acceptable range. This showed that IM guides are reasonably accurate in producing desired angles in the coronal plane but less accurate in the sagittal plane, with a greater number of outliers in femoral flexion and posterior slope. Surgeons need to be aware of potential cutting errors when using IM guides as they affect the overall alignment of the implant, and real-time verification technology is available to verify the accuracy of the cuts.

Innovative protection schemes through hardware-in-the-loop dynamic testing.

In microgrid environments, the behaviour of Distributed Generation (DG) during fault conditions varies significantly based on DG types and penetration levels. Conventional Overcurrent Relays (OCRs) with standard time-current characteristics may exhibit limitations during excessive fault scenarios, leading to OCR operating delays and mis-coordination within the microgrid. This study proposes a novel constraint on the maximum Current Multiplier Setting (CMS) and utilizes Water Cycle Algorithm (WCA) and Particle Swarm Optimization (PSO) techniques to optimize the Time Multiplier Setting (TMS). Comparative dynamic analysis through real-time validation shows that the non-standard OCR approach outperforms the standard IEC scheme across all grid operation modes with different type, size and location of DGs. For instance, under F1 conditions, the tripping time of OCR1 was reduced from 0.0226 seconds (IEC) to 0.000981 seconds (non-standard). HIL results further affirm the efficacy of the proposed scheme. The optimization process, implemented in MATLAB and validated using ATP/EMTP simulations and SIPROTEC 7SJ62 relays, demonstrates enhanced microgrid protection coordination, improving system reliability and performance.

Reliability analysis of a grid-connected hybrid renewable energy system using hybrid Monte-Carlo and Newton Raphson methods

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.

Dysregulation of tetrahydrobiopterin metabolism in myalgic encephalomyelitis/chronic fatigue syndrome by pentose phosphate pathway.

Tetrahydrobiopterin (BH4) and its oxidized derivative dihydrobiopterin (BH2) were found to be strongly elevated in ME/CFS patients with orthostatic intolerance (ME + OI). However, the molecular mechanism of biopterin biogenesis is poorly understood in ME + OI subjects. Here, we report that the activation of the non-oxidative pentose phosphate pathway (PPP) plays a critical role in the biogenesis of biopterins (BH4 and BH2) in ME + OI subjects. Microarray-based gene screening followed by real-time PCR-based validation, ELISA assay, and finally enzyme kinetic studies of glucose-6-phosphate dehydrogenase (G6PDH), transaldolase (TALDO1), and transketolase (TK) enzymes revealed that the augmentation of anaerobic PPP is critical in the regulations of biopterins. To further investigate, we devised a novel cell culture strategy to induce non-oxidative PPP by treating human microglial cells with ribose-5-phosphate (R5P) under a hypoxic condition of 85%N2/10%CO2/5%O2 followed by the analysis of biopterin metabolism via ELISA, immunoblot, and dual immunocytochemical analyses. Moreover, the siRNA knocking down of the taldo1 gene strongly inhibited the bioavailability of phosphoribosyl pyrophosphate (PRPP), reduced the expressions of purine biosynthetic enzymes, attenuated GTP cyclohydrolase 1 (GTPCH1), and suppressed subsequent production of BH4 and its metabolic conversion to BH2 in R5P-treated and hypoxia-induced C20 human microglia cells. These results confirmed that the activation of non-oxidative PPP is indeed required for the upregulation of both BH4 and BH2 via the purine biosynthetic pathway. To test the functional role of ME + OI plasma-derived biopterins, exogenously added plasma samples of ME + OI plasma with high BH4 upregulated inducible nitric oxide synthase (iNOS) and nitric oxide (NO) in human microglial cells indicating that the non-oxidative PPP-induced-biopterins could stimulate inflammatory response in ME + OI patients. Taken together, our current research highlights that the induction of non-oxidative PPP regulates the biogenesis of biopterins contributing to ME/CFS pathogenesis.

Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation

Photovoltaic (PV) systems are prone to various faults, including short-circuit, open-circuit, partial shading, and inverter bypass diode issues, which reduce power output and can damage components. This study presents an innovative fault detection and online monitoring technique for grid-connected PV (GCPV) systems, combining Internet of Things (IoT) technology with a one-dimensional convolutional neural network (1D-CNN) deep learning approach. The method involves developing a temperature-dependent PV system model using series resistance and ideality factor, capturing real-time data from a 15kWp GCPV system with optimally placed sensors to minimize sensor count while maintaining data accuracy, and validating the model through MATLAB/Simulink simulations and real-time experiments under various fault scenarios. The collected data is used to train the 1D-CNN model to classify different fault types. The model is then implemented on an IoT platform for real-time monitoring and fault detection, displaying system status and alerts via a dashboard. The proposed system achieves a high fault detection accuracy of 98.15 % and 93.12 % during cyberattacks, with an uncertainty of ±4 %, significantly enhancing fault detection reliability and efficiency compared to existing methods. The IoT dashboard provides an effective tool for monitoring system performance and issuing alerts under abnormal conditions.

Effects of Castanopsis echinocarpa on Sensorineural Hearing Loss via Neuronal Gene Regulation.

Sensorineural hearing loss (SNHL), characterized by damage to the inner ear or auditory nerve, is a prevalent auditory disorder. This study explores the potential of Castanopsis echinocarpa (CAE) as a therapeutic agent for SNHL. In vivo experiments were conducted using zebrafish and mouse models. Zebrafish with neomycin-induced ototoxicity were treated with CAE, resulting in otic hair cell protection with an EC50 of 0.49 µg/mL and a therapeutic index of 1020. CAE treatment improved auditory function and protected cochlear sensory cells in a mouse model after noise-induced hearing loss (NIHL). RNA sequencing of NIHL mouse cochleae revealed that CAE up-regulates genes involved in neurotransmitter synthesis, secretion, transport, and neuronal survival. Real-time qPCR validation showed that NIHL decreased the mRNA expression of genes related to neuronal function, such as Gabra1, Gad1, Slc32a1, CaMK2b, CaMKIV, and Slc17a7, while the CAE treatment significantly elevated these levels. In conclusion, our findings provide strong evidence that CAE protects against hearing loss by promoting sensory cell protection and enhancing the expression of genes critical for neuronal function and survival.

Performance assessment of VSC-based HVDC system in asynchronous grid interconnection: Offline and real-time validation of control design with symmetric optimum PI tuning

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer.High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system.The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation.The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.