Improve System Performance Research Articles

Impact of Grid‐Side Converter Filter on the Small‐Signal Stability of a Grid‐Connected DFIG‐Based Wind‐Driven System

ABSTRACTThe incorporation of doubly fed induction generators (DFIGs) into wind‐driven systems (WDS) has become increasingly significant because of their efficient and adaptable power generation capabilities. The proposed system consists of a wind turbine (WT), wind generator, rotor‐side converter (RSC), grid‐side converter (GSC), grid‐side filter (GSF), and a line network. The GSF can have a significant impact on the performance and small signal stability (SSS) of grid‐connected DFIG‐based wind‐driven systems (WDS). This work offers a comprehensive SSS analysis of the proposed system, incorporating GSF dynamics into the modeling process. This study connects the DFIG‐based WDS with a single‐machine infinite bus (SMIB) test system and uses an in‐depth simulation approach to evaluate the effect of the voltage and current dynamics of the grid‐side LCL (inductor‐capacitor‐inductor) filter on SSS under different operating conditions. The analysis employs eigenvalue and participation factor techniques to examine how GSF dynamics influence the SSS of the system. Further, the dynamic behavior of rotor speed, electrical torque, DC‐link voltage, and voltage at the point of common coupling (PCC) is investigated in response to various step disturbances in wind speed and infinite bus voltage. The proposed model enhances the system's dynamic performance and improves the current and power quality of the DFIG by reducing total harmonic distortion (THD). All the simulation studies were conducted in a MATLAB/SIMULINK environment. Finally, the simulation results were validated for real‐time applications through real‐time control simulations on the OPAL‐RT platform. This study offers valuable insights into the dynamics of GSFs in DFIG‐based WDS, providing practical recommendations for improving system performance and grid integration.

Optimizing Data Flow in Multi-Cloud Environments: A Technical Deep Dive

The widespread adoption of multi-cloud strategies has fundamentally transformed enterprise computing architectures, introducing opportunities and challenges in modern IT environments. This article examines data flow optimization between multiple cloud providers, focusing on performance constraints, integration complexities, and security requirements. The article investigates advanced optimization techniques, including direct cloud interconnection, data fabric implementation, intelligent replication strategies, and edge computing integration. Through this article, enterprise deployments across various sectors, the article demonstrates that properly implemented multi-cloud optimization strategies significantly enhance operational resilience, reduce costs, and improve system performance. The article highlights the importance of standardized APIs, comprehensive monitoring frameworks, and emerging AI-driven optimization approaches in achieving efficient multi-cloud operations. This article provides valuable insights for organizations seeking to optimize their multi-cloud environments while maintaining security and compliance requirements.

AI-driven video summarization for optimizing content retrieval and management through deep learning techniques

With the rapid advancement of artificial intelligence, questions are increasingly being raised by stakeholders regarding how such technologies can enhance the environmental, social, and governance outcomes of organizations. In this study, challenges related to the organization and retrieval of video content within large, heterogeneous media archives are addressed. Existing methods, often reliant on human intervention or low-complexity algorithms, are observed to struggle with the growing demands of online video quantity and quality. To address these limitations, a novel approach is proposed, where convolutional neural networks and long short-term memory networks are utilized to extract both frame-level and temporal video features. Residual networks 50 (ResNet50) is integrated for enhanced content representation, and two-frame video flow is employed to improve system performance. The framework achieves precision, recall, and F-score of 79.2%, 86.5%, and 83%, respectively, on the YouTube, EPFL, and TVSum datasets. Beyond technological advancements, opportunities for effective content management are highlighted, emphasizing the promotion of sustainable digital practices. By minimizing data duplication and optimizing resource usage, scalable solutions for large media collections are supported by the proposed system.

Performance improvement of a novel trans-critical CO2 air conditioner system with various cabin loads by using surrogate models and genetic algorithms

Performance improvement of a novel trans-critical CO2 air conditioner system with various cabin loads by using surrogate models and genetic algorithms

A review of heat transfer enhancement techniques in power systems

The optimization of modern power systems demands that they enhance heat transfer. Analysis has demonstrated the passive use of nanofluids and surface roughness to greatly enhance thermal conductivity as well as increase heat transfer rates, but without requiring the input of additional energy. Meanwhile, active methods, from pulsating flows to electromagnetic fields, have been shown to decrease thermal resistance and improve system performance. Further significant improvements in efficiency are achieved when hybrid approaches combine both passive and active strategies that overcome the limitations of each method individually. This review critically analyses these heat transfer enhancement methods on the grounds of their principles, mathematical models, and experimental results. The paper presents a detailed understanding of how these techniques are applied in a variety of domains, including power generation, renewable energy systems, and high-performance electronics, based on the incorporation of evidence from 20 validated sources. These findings present the transformative potential of these advancements to address fundamental challenges including energy efficiency, component miniaturization, and environmental sustainability.

Modernizing enterprise architecture: A guide to microservices migration and hybrid cloud integration

The migration from monolithic to microservices architecture represents a transformative journey in enterprise software development, complemented by the adoption of hybrid cloud technologies. This comprehensive article explores the strategic approaches, implementation patterns, and best practices for successful architectural transformation. The article examines key aspects, including preliminary analysis requirements, domain-driven design implementation, technical considerations for database and API management, and the integration of hybrid cloud technologies. Through a detailed examination of containerization, orchestration, and observability practices, this research provides organizations with a structured framework for modernizing their enterprise architecture while maintaining operational efficiency and system reliability. The article incorporates real-world implementation cases across various sectors, demonstrating measurable improvements in development agility, system performance, and business outcomes.

Controlling the energies of the single-rotor large wind turbine system using a new controller

In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators. Key features of the proposed regulator include its simplicity, cost-effectiveness, ease of implementation, reduced number of gains, and rapid dynamic response.This regulator enhances the direct power control (DPC) approach, as it integrates two tailored controllers alongside a pulse width modulation strategy to manage the machine inverter. The DPC strategy incorporating the proposed controller was implemented and tested using MATLAB, with various simulations to evaluate its performance and effectiveness. The proposed regulator demonstrated a significant improvement over the PI regulator, with reductions in active power ripples of 69%, 61.70%, and 59.14% across different tests. Additionally, the steady-state error of reactive power was reduced by 54.84%, 85.23%, and 62.68%, and the total harmonic distortion of current decreased by 48.12%, 50.55%, and 56.05%. These results underscore the high efficiency, robustness, and effectiveness of the proposed controller in improving system performance compared to conventional PI regulators. The controller’s outstanding performance makes it a promising solution for broader industrial applications.

Tren Algoritma Penjadwalan Tugas Pada Cloud Computing: Systematic Review Literature

Task scheduling in cloud computing environments is a crucial aspect in optimizing resource allocation and improving system efficiency. This research aims to analyze trends in task scheduling algorithms in cloud computing using a Systematic Literature Review (SLR) approach on various scientific publications published between 2018 and 2025. The results of the study show that Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) algorithms are the most commonly used methods in solving task scheduling problems. PSO stands out as an effective algorithm due to its ability to find global optimal solutions, handle non-linear and multimodal problems, and its efficiency in managing computational resources. Additionally, various studies have shown that optimization of scheduling algorithms can be achieved through a combination or modification of existing methods to improve system performance. This study provides in-depth insights into the development of scheduling algorithms in cloud computing and opens up opportunities for further research in developing more innovative and adaptive approaches.

Electromagnetic Field Modeling and Optimization Design of Axial Magnetic Flux Eddy Current Brake Considering Non‐Ideal Factors

Accurate analytical calculation of electromagnetic field is the basis of research and optimization of axial magnetic flux eddy current brake, and structural optimization design is the key to improving system performance and reducing engineering construction costs. In order to establish an accurate analytical calculation model of electromagnetic field of axial magnetic flux eddy current brake, based on the two‐dimensional analytical calculation model of electromagnetic field, this paper comprehensively considers the influence of non‐ideal factors such as core saturation, transverse dynamic end effect and secondary skin effect on the electromagnetic characteristics of axial magnetic flux eddy current brake, and obtains an improved analytical calculation model of electromagnetic field by introducing corresponding correction coefficients, and verifies the accuracy of the analytical calculation model of electromagnetic field by comparing with the numerical calculation results of three‐dimensional electromagnetic field finite element model. On this basis, an improved multi‐objective particle swarm optimization algorithm is adopted to optimize the system design, so as to increase the electromagnetic braking torque, reduce the electromagnetic axial force and reduce the secondary mass. Finally, the accuracy of the above electromagnetic field analytical calculation model and optimization design results is verified on the experimental research platform of axial magnetic flux eddy current brake, which can provide theoretical basis for subsequent researchers to design and optimize the electromagnetic scheme. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Architectures and Optimization Strategies for Real-Time Machine Learning Recommendation Systems: A Systematic Review of Scalability Challenges

This article comprehensively analyzes the challenges and solutions in deploying real-time machine learning recommendation systems at scale. The article examines the critical trade-offs between model complexity, inference latency, and system scalability that impact modern recommendation architectures. The article investigates three primary dimensions: infrastructure optimization, model serving strategies, and resource utilization patterns. The article proposes a novel framework for balancing these competing requirements through a combination of distributed computing architectures, hybrid model deployment approaches, and intelligent caching mechanisms. The findings demonstrate that implementing a multi-tiered serving architecture with dynamic resource allocation significantly improves system performance while maintaining recommendation quality. The article also explores emerging optimization techniques, including model quantization, feature store architectures, and adaptive serving strategies. The article contributes to the field by providing a systematic approach to designing and implementing real-time recommendation systems that can effectively handle high-concurrency workloads while delivering personalized suggestions within strict latency constraints. The results offer valuable insights for practitioners and researchers working on large-scale recommendation systems, particularly in environments where real-time performance is crucial.

Performance Improvement of a Standalone Hybrid Renewable Energy System Using a Bi-Level Predictive Optimization Technique

Performance Improvement of a Standalone Hybrid Renewable Energy System Using a Bi-Level Predictive Optimization Technique

A Novel Flip-Filtered Orthagonal Frequency Division Multiplexing-Based Visible Light Communication System: Peak-to-Average-Power Ratio Assessment and System Performance Improvement

Filtered orthogonal frequency division multiplexing (F-OFDM), employed in visible light communication (VLC) systems, has been considered a promising technique for overcoming OFDM’s large out-of-band emissions and thus reducing bandwidth efficiency. However, due to Hermitian symmetry (HS) imposition, a challenge in VLC involves increasing power consumption and doubling inverse fast Fourier transform IFFT/FFT length. This paper introduces the non-Hermitian symmetry (NHS) Flip-F-OFDM technique to enhance bandwidth efficiency, reduce the peak–average-power ratio (PAPR), and lower system complexity. Compared to the traditional HS-based Flip-F-OFDM method, the proposed method achieves around 50% reduced system complexity and prevents the PAPR from increasing. Therefore, the proposed method offers more resource-saving and power efficiency than traditional Flip-F-OFDM. Then, the proposed scheme is assessed with HS-free Flip-OFDM, asymmetrically clipped optical (ACO)-OFDM, and direct-current bias optical (DCO)-OFDM. Concerning bandwidth efficiency, the proposed method shows better spectral efficiency than HS-free Flip-OFDM, ACO-OFDM, and DCO-OFDM.

Performances analysis and improvement of mechanical system using the fractional order adaptive PID controller

Mechanical systems find applications across various scientific disciplines, particularly in the domain of control strategies, which has drawn significant interest from researchers. A fractional adaptive PID (FAPID) controller is suggested as a means to improve system performance in this study—specifically, rise time, settling time, and overshoot—by incorporating fractional-order integrators and differentiators into the traditional adaptive PID feedback mechanism. The performance and efficiency of the suggested FAPID controller are matched with those of the traditional adaptive PID controller in order to show the validity of this method. Analyzes and numerical simulations help to find the most effective controller. Regarding settling time, rising time, overshoot, and mean absolute error, the fractional-order PID controller performs better than its classical equivalent. Furthermore, extending this method to other fractional and integer-order systems helps to maximize noise rejection and performance.

Next-generation cybersecurity strategies for 3D printing in high-intensity manufacturing

Abstract This research examines the key factors that influence optimal results in 3D printing, including print speed, temperature, layer thickness and material selection. Specifically, it explores the impact of materials such as acrylonitrile butadiene styrene (ABS), nylon, polylactic acid (PLA), and polyethylene terephthalate glycol (PETG) on the 3D printing process and the decision-making involved in achieving the best results. It provides a comprehensive review of the impact of these variables on print quality, drawing on existing studies and experimental data. The findings indicated that print speed of 66 mm s−1, temperature of 218 °C, layer thickness of 0.2 mm and use of PLA material gave the best results. In addition, the research explores the use of Support Vector Machine (SVM) modeling to predict and improve print quality based on these parameters. Research confirms that through statistical analysis of test data, optimal print settings can be effectively identified. Beyond print quality, the research also addresses critical cybersecurity vulnerabilities in 3D printing systems, particularly related to their integration with IT networks. It highlights the importance of analyzing network traffic to detect vulnerabilities, improve system performance, and strengthen cybersecurity measures. The application of machine learning techniques for anomaly detection is explored as a means of developing risk management strategies and ensuring compliance with auditing standards. Finally, the research discusses the role of blockchain technology in securing machine-to-machine communication (M2M) within the 3D printing environment, as well as ensuring data integrity and confidentiality.

Enterprise Microservices: Revolutionizing Telecom Network Architecture

This comprehensive article explores the transformative impact of enterprise microservices architecture in the telecommunications industry through the implementation of the UltraVailable Network Service (UVNS) project. The article examines how microservices address challenges related to critical network scalability, availability, and operational efficiency. The article demonstrates the significant advantages of microservices over traditional monolithic architectures through a detailed analysis of automated provisioning, billing systems, and advanced monitoring capabilities. The implementation showcases innovations in dynamic resource allocation, high availability features, and data management systems supported by artificial intelligence and machine learning technologies. The results reveal substantial improvements in service delivery, operational costs, and system performance, establishing a new benchmark for telecommunications infrastructure management.

Industrial Design of Type-1 and Interval Type-2 Fuzzy Logic Control

This paper focuses on the design of type-1 and interval type-2 (IT2) PID fuzzy logic controllers (FLC) for ensuring - by a programmable logic controller (PLC) - a high-performance real-time liquid level control in a carbonization column (CCl) for soda production. Firstly, Takagi-Sugeno-Kang models - derived via genetic algorithms parameter optimizations, experimental data and simulations for the basic and the worst CCl loads - are studied at different operation points, and the worst Ziegler-Nichols (ZN) model is assessed. Next, two-input fuzzy units are designed - assuming various membership functions (MF) and uncertainties - and the greatest linearization gain is computed. Based on it and the ZN worst plant model, the parameters of the FLC input pre-processing differentiator and the PI post-processing are empirically tuned. Finally, a PLC oriented analytical description of the IT2 MF, fuzzy rules and type-reduction is suggested. The designed FLC systems are studied via simulation to determine the factors that have the greatest impact on the system performance improvement. The obtained results unveil that the tuned FLC outperforms the tuned linear PI. Better system performance is achieved by a small number of MF with large support ensuring economical PLC presentation.

Artificial Intelligence-Based Direct Power Control for Power Quality Improvement in a WT-DFIG System via Neural Networks: Prediction and Classification Techniques

This paper discusses the improvement of power quality injected into the AC grid. This approach is achieved by enhancing the quality of injected power signals and mastering the active and reactive power exchanged between the DFIG based wind turbine (WT-DFIG) and the electrical grid, resulting in an improvement of the overall system performance and efficiency. This study includes all WT-DFIG operating modes, successively and continuously, as well as all local reactive power compensation modes. Therefore, novel control strategies are proposed in this paper for wind energy conversion systems based on artificial intelligence techniques. These techniques include Neural Network Prediction (PNN-DPC) and Classification (CNN-DPC). They aim to eliminate the drawbacks and difficulties associated with conventional Direct Power Control (C-DPC), while retaining its advantages. The paper also provides a thorough explanation of the mathematical models for neural network techniques and WT-DFIG system models. The MATLAB/Simulink environment is used to investigate the performance of the proposed techniques under different conditions and operating modes related to different scenarios. The results reveal a significant reduction in the ripple of the generated active power and the compensated local reactive power, better quality of the generated signal currents and a remarkable reduction in the current total harmonic distortion (THD). Furthermore, compared to C-DPC, PNN-DPC achieves a reduction of 72.07% in active power ripples, 77.07% in reactive power ripples, and 76.79% in current Total Harmonic Distortion (THD). CNN-DPC shows similar improvements with 72.04%, 77.13%, and 76.54% of reductions respectively. In addition, CNN-DPC slightly outperforms PNN-DPC. Nevertheless, both proposed control techniques show significant improvements in all characteristics compared to other methods. Consequently, the proposed control strategies indicate that artificial intelligence has the potential to improve the power quality and performance of wind power conversion system.

Control Performance Improvement of PMSM Direct-Drive Servo System at Low Speeds

Control Performance Improvement of PMSM Direct-Drive Servo System at Low Speeds

Performance analysis and improvement of turbine-powered air cycle system

Performance analysis and improvement of turbine-powered air cycle system

NUMERICAL INVESTIGATION AND NEW INTELLIGENT SYSTEM FOR PERFORMANCE IMPROVEMENT OF A RADIAL IMPULSE TURBINE FOR WAVE ENERGY CONVERSION

This study presents a numerical investigation into the influence of rotor blade geometry on the performance of oscillating water column (OWC) impulse turbines for wave energy conversion. A numerical model was developed and validated using experimental data for both circular and elliptical rotor profiles. Simulations were conducted for inhalation and exhalation modes of turbine operation, focusing on flow angles, flow coefficients, and losses coefficients in various turbine components. Results reveal significant differences in performance and flow behavior between turbines with circular and elliptical blade profiles, particularly in the inhalation mode. Circular blade profiles demonstrated superior performance and favorable flow characteristics, whereas elliptical profiles exhibited more pronounced flow separation attributed to higher curvature variation. The findings underscore the critical role of blade profile in determining turbine performance, highlighting the importance of optimizing blade design for enhanced efficiency. Building upon these results, an integration of an intelligent control system for guide vane geometric angles is proposed to ensure optimal alignment with downstream flow angles throughout the cycle. This study contributes to the understanding of turbine performance in OWC systems and provides insights for future design optimization efforts aimed at maximizing energy conversion efficiency.