Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Multi-Objective Particle Swarm Optimization for Enhancing Chiller Plant Efficiency and Energy Savings
Get PDFThis study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings
IoT-AI in Healthcare: A Comprehensive Survey of Current Applications and Innovations
Get PDFThe convergence of IoT and AI technology has the capacity to revolutionize healthcare by facilitating the gathering of real-time data and employing sophisticated analytics for tailored medical solutions. This survey provides an in-depth examination of IoT-AI applications in healthcare, specifically focusing on wearable devices such as smart bands and wristbands, as well as health monitoring systems. We present the core principles of IoT and AI, examining their synergistic integration in healthcare environments. The taxonomy of IoT-AI-based healthcare systems is comprehensive and classifies them according to their architectural components, data processing algorithms, and application domains. The survey showcases distinctive achievements, including novel methodologies for combining data and making predictions, frameworks for improving patient monitoring, and inventive methods for delivering healthcare remotely. We offer a comprehensive examination of key challenges such as data privacy, interoperability, and regulatory compliance, and analyze their specific effects on the implementation and efficacy of IoT-AI healthcare systems. The comparison analysis encompasses measures such as system performance, accuracy, and user satisfaction, providing valuable insights into the strengths and limitations of different techniques. In addition, we analyze developing patterns and clearly outline future areas of study, such as the enhancement of stronger security protocols, the use of blockchain technology to ensure data integrity, and the progress in AI algorithms to achieve more precise diagnoses. Emerging trends such as Digital Twins and SLUC are identified as promising avenues for future research. In conclusion, this study provides a detailed framework that enhances the understanding of IoT-AI healthcare systems and offers practical insights for improving healthcare practices and guiding technology adoption
A Novel Predictive Voltage Control Technique for a Grid Connected Five Phase Permanent Magnet Synchronous Generator
Get PDFThis study focuses on developing an effective control strategy to enhance the dynamics of a wind turbine grid-connected five-phase permanent magnet synchronous generator (PMSG). To visualize the superior performance of the newly proposed controller, the generator's performance is evaluated with another traditional predictive control scheme: predictive torque control (PTC). However, the vector control principle is applied to the GSC converter. The PTC has limitations such as significant ripple, substantial load commutation, and the inclusion of a weighting element in its cost functions. The proposed predictive methodology aims to overcome limitations, uses a simple cost function, and doesn't require weighting elements to address concerns about stability errors. Comparing the proposed predictive voltage controller (PVC) to the PTC, the findings show that the suggested PVC has many benefits, including faster dynamic response, a simpler control structure, fewer ripples, reduced current harmonics, low computation burdens, and robustness, so the generated power affects system efficiency, leading to improved power quality and reduced switching losses, enhancing power converters efficiency and their switches lifespan, this fact is verified mathematically as the total harmonic distortion (THD) has reduced to 1.346% average percentage for the proposed controller. However, the THD of the PTC is 3.05%. In addition, the study examines the incorporation of pitch angle control (PAC) and maximum power point tracking (MPPT). These controls restrict the consumption of wind energy when the generator speed surpasses its rated speed and optimize the extraction of wind energy during periods of low wind availability. In summary, the proposed PVC-enhanced control system reveals superior performance in dynamic response, control simplicity, current quality, and computational efficiency compared to other methods
Fuzzy Control for Spacecraft Orbit Transfer with Gain Perturbations and Input Constraint
Get PDFThis paper presents the problem of fuzzy guaranteed cost tracking control for spacecraft orbit transfer with parameter uncertainties and additive controller gain perturbations and subject to input constraints, and guaranteed cost function. The goal is to perform a planar orbit transfer in a circular orbit, focusing on minimizing fuel usage while accounting for uncertainties in both the plant and controller. Spacecraft dynamics is based on the Keplerian two-body problem using polar coordinates, which allows long-distance maneuvers in circular orbit when the well-known Clohessy-Wiltshire (C-W) equation is restricted by limited-distance maneuvers. To approximate the nonlinearities in the dynamical equation of motion, a Takagi-Sugeno (T-S) fuzzy model is proposed and a linearized model is established for the output tracking problem of the orbit transfer process. Issue related to the absence of a single equilibrium point in the nonlinear system, a gain-scheduling technique based on multiple operating points is employed to develop the (T-S) fuzzy model through the fuzzy approach. Based on the parallel distributed compensation (PDC) approach, sufficient conditions for a fuzzy non-fragile guaranteed cost control are derived. Using the Lyapunov theory, the controller objectives are formulated through linear matrix inequality (LMIs) which allows the system to be transferred into a convex optimization problem. The designed controller effectively accomplishes the orbit transfer process with minimal fuel consumption and maintains the performance level below a specified upper bound. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method
Performance of New Control Strategy of Dual Stator Induction Generator System Applied in Wind Power Generation
Get PDFIn order to improve the quality of energy and reduce the harmonics produced by the power electronics converters, it is proposed and developed in this article the direct torque control, in which the flux and torque are estimated from the only measurable electrical quantities. The direct torque control DTC method, to enhance the dynamic and static performances as well as the robustness of the control of the Wind Energy Conversion System (DSIG). DTC is a control technique that exploits the possibility of imposing torque and flux on alternating current machines in a decoupled manner, once powered by a voltage inverter without current regulation made by a feedback loop, ensuring a decoupling, similar to that obtained from a vector control. The technique involved rapid torque response, insensitivity to parametric variation, in particular the machine's rotor time constant and systematic suitability for control without speed sensor. The main function of the generator side controller is to track the maximum power through controlling the rotational speed of the wind turbine using PI controller. The performance and the effectiveness of theΓΒ ΓΒ proposed control system are tested via simulation results in terms of reference tracking, and robustness against parameters variations of the DSIG. Simulation results for 1.5 MW DSIG control show robust with respect to the parametric variation 2 Rs, 1,5 Rs et 0.5 Rs, and fast dynamic behavior of system, with the temps of response is 0.02 s, active power extracted 0.15 MW with lambda 9 and Cp 0,5 that the wind turbine can operate at its optimum power point for a wide range of wind speed and power quality can be greatly improved
Performance analysis of random forest on quartile classification journal
Get PDFJournals play a pivotal role in disseminating scientific knowledge, housing a multitude of valuable research articles. In this digital age, the evaluation of journals and their quality is essential. The SCImago Journal Rank (SJR) stands as one of the prominent platforms for ranking journals, categorizing them into five index classes: Q1, Q2, Q3, Q4, and NQ. Determining these index classes often relies on classification methodologies. This research, drawing inspiration from the Cross-Industry Standard Process for Data Mining (CRISP-DM), seeks to employ the Random Forest method to classify journals, thus contributing to the refinement of journal ranking processes. Random Forest stands out as a robust choice due to its remarkable ability to mitigate overfitting, a common challenge in machine learning classification tasks. In the context of approximating SJR index classes, Random Forest, when utilizing the Gini index, exhibits promise, albeit with an initial accuracy rate of 62.12%. The Gini index, an impurity measure, enables Random Forest to make informed decisions while classifying journals into their respective SJR index classes. However, it is worth noting that this accuracy rate represents a starting point, and further refinement and feature engineering may enhance the model's performance. This research underscores the significance of machine learning techniques in the domain of journal classification and journal-ranking systems. By harnessing the power of Random Forest, this study aims to facilitate more accurate and efficient categorization of journals, thereby aiding researchers, academics, and institutions in identifying and accessing high-quality scientific literature
Canopy garden model for synergy of land and sea area on Papan Island in Tojo Una Una Regency, Central Sulawesi
Get PDFPulau Papan is one part of the Togean archipelago, Kabupaten Tojo Una Una. Initially, the Pulau Papan area was part of Tiga Pulau Village, Walea Islands District and Malenge Village, Talatako District, because the people who lived in this area came from these two villages. However, in 2011 Pulau Papan was expanded along with the increase in the number of family heads by 638 people and became part of the Kadoda Village area, Talatako District. The aim of this research is to build synergy in destination development between sea space and land/island space by creating regional nodes that are beautiful and function for the public. The public function that is formed can be utilized by the community both as a public space and a functional area that is beneficial for the residents of Pulau Papan, for example a green area containing vegetables for the residents. This vegetable green area is needed because of the limited vegetables on Pulau Papan. The solution proposed in this research is to design a canopy garden model that can become a green structure in the Pulau Papan area. The canopy garden will function to synergize the arrangement of land and sea areas on Pulau Papan. This canopy garden modeling was obtained through a survey process of the Pulau Papan area, then determining the meeting points for the mobility of residents on Pulau Pulau Papan. The public function that is formed can be utilized by the community both as a public space and a functional area that is beneficial for the residents ofΓΒ Pulau Papan, for example a green area containing vegetables for the residents
Advancements in precast concrete sandwich panels for load bearing structures
Get PDFConcrete sandwich panels consist of two concrete layers separated by an insulating foam core, offering thermal insulation, structural strength, and fire resistance. This study investigates sustainable precast concrete sandwich panels made with industrial waste materials like limestone slurry, quarry waste, and basalt fiber as shear connectors. The research evaluates the flexural and axial strength behavior of these panels and explores strategies to improve their structural performance. The panels were fabricated with outer concrete layers, an expanded polystyrene (EPS) insulation core, and basalt fiber connectors. Flexural tests using four-point bending and axial compression tests were conducted on panels with varying concrete layer thicknesses and basalt fiber widths. Findings revealed panels with thicker outer concrete layers (35mm) and wider basalt fiber connectors (11.5mm) exhibited higher cracking loads, load-hardening behavior, and increased ductility compared to thinner layers and narrower connectors. The axial test showed premature failure at the top and bottom quarters. Thicker concrete layers and wider basalt fiber connectors enhanced crack control, load distribution, and ductile behavior under flexural loading. Strengthening measures like additional reinforcement, proper anchorage detailing, and increased shear reinforcement at the end regions are recommended to improve axial load-bearing capacity and prevent premature end failures. ΓΒ The PCSP demonstrated up to 40% cost savings over commercial products while providing better thermal insulation than conventional brick masonry due to the EPS core. Overall, the study promotes developing sustainable, energy-efficient, and cost-effective load-bearing sandwich panel systems.Γ
A Review of Deep Learning-Based Defect Detection and Panel Localization for Photovoltaic Panel Surveillance System
Get PDFAs the photovoltaic (PV) systems expands globally, robust defect detection and precise localization technologies becomes crucial to ensure their operational efficiency. This review introduces an integrated deep learning framework that leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and You Only Look Once (YOLO) algorithms to enhance defect detection in solar panels. By integrating these technologies with Global Positioning System (GPS) and Real-Time Kinematic (RTK) GPS, the framework achieves unprecedented accuracy in defect localization, facilitating efficient maintenance and monitoring of expansive solar farms. Specifically, CNNs are employed for their superior feature detection capabilities in identifying defects such as microcracks and delamination. RNNs enhance the framework by analyzing time-series data from panel sensors, predicting potential failure points before they manifest. YOLO algorithms are utilized for their real-time detection capabilities, allowing for immediate identification and categorization of defects during routine inspections. This review's novel contribution lies in its use of an integrated approach that combines these advanced technologies to not only detect but also accurately localize defects, significantly impacting the maintenance strategies for PV systems. The findings demonstrate an improvement in detection speed and localization accuracy, suggesting a promising direction for future research in solar panel diagnostics. The review provided aims to refine surveillance systems and improve the maintenance protocols for photovoltaic installations, ensuring longevity, durability and efficiency in energy production
Performance Enhancement of Dual-Star Induction Machines Using Neuro-Fuzzy Control and Multi-Level Inverters: A Comparative Study with PI Controllers
Get PDFThis paper proposes a hybrid speed control strategy for Dual-Star Induction Machines (DSIMs) supplied by Multi-Level Inverters (MLIs). The proposed approach integrates a Neuro-Fuzzy Controller (NFC) with an Indirect Field-Oriented Control (IFOC) technique, leveraging the adaptive learning capabilities of an Artificial Neural Network (ANN) to optimize the NFC parameters. This strategy achieves significant enhancements in speed regulation performance, including a 20% reduction in settling time, a 15% decrease in overshoot, and minimized steady-state error. The NFC's online adaptive learning capability enables real-time adjustments, outperforming the PI controller in handling rotor resistance variations and load disturbances. Simulation results demonstrate a 35% reduction in torque ripple and a 20% improvement in speed regulation compared to PI controllers. The NFC also exhibits faster response times during torque change and remains unaffected by 50% rotor resistance variations. Additionally, the NFC controller achieves up to 51% reduction in Total Harmonic Distortion (THD) compared to the PI controller.ΓΒ Increasing the inverter voltage level from m=2 to m=7 significantly reduces electromagnetic torque ripple, demonstrating a direct correlation between higher inverter levels and improved torque ripple performance. These improvements position the NFC-based strategy as a promising solution for industrial applications requiring precise speed control, such as robotics, electric vehicles, and automation systems