Optimal Load Distribution Research Articles

Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA) for Cooperative Manipulators Applications

Cooperative manipulators face challenges related to an inadequate distribution of external loads and a decrease in Dynamic Load Carrying Capacity (DLCC). Understanding the impact of optimal load distribution on power consumption, load carrying capacity, and gripper error is crucial. This paper presents the Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA), a novel method that achieves optimal external load distribution. ISPLLDA dynamically distributes the external load among manipulators based on torque-bearing capacity and actuator position. Additionally, nonlinearity in system dynamics introduces uncertainties, leading to incorrect DLCC evaluation, increased error, and higher actuator power consumption. To address this, a Radial Basis Function Neural Network (RBFNN) accurately determines actuator saturation limits in the presence of uncertainty, enabling correct estimation of system dynamics and external disturbances. ISPLLDA ensures near-simultaneous saturation of all manipulators' actuators, maximizing their capacity utilization. The proposed method is validated through simulations and experimental tests on cooperative manipulators. Results demonstrate a 17% increase in load-carrying capacity, as well as more than 35% improvement in error and torque indexes compared to the Lagrange multipliers method.

Optimization Algorithms for Sustainable Operation of Multi-Unit Hydropower Plants

The work presented in this article concerns numerical studies on optimization methods used for the sustainable utilization of the energy potential of water, converting it into electricity in a hydropower plant equipped with more than one unit. These methods allow for maximization of production in given hydrological conditions, leading to the balanced, lossless, and environmentally friendly use of the renewable energy source that is water. Methods are selected from three groups, i.e., analytical, enumeration, and randomized. The results of calculations of optimal points of selected test functions carried out using the Broyden–Fletcher–Goldfarb–Shanno Limited-Memory Version (L-BFGS-B), Explicit Complete Enumeration (ECE), and Genetic Algorithm (GA) methods provided basic information on the features of these methods. Based on these tests, the GA method was selected to solve the problem of the optimal load distribution in a hydropower plant equipped with three identical hydro units. The defined optimization problem consisted of finding a configuration of hydro units in operation that would guarantee the maximum efficiency of the power plant under the imposed hydrological conditions. During the numerical studies, a number of calculations were performed to identify the impact of procedures and parameters characteristic of the optimization methods on the obtained results. Particular attention was paid to the GA method and penalty functions, enabling the elimination of results from the area of prohibited solutions.

Surgical planning in HTO – alternative approaches to the Fujisawa gold-standard

Background Presurgical planning of the correction angle plays a decisive role in a high tibial osteotomy, affecting the loading situation in the knee affected by osteoarthritis. The planning approach by Fujisawa et al. aims to adjust the weight-bearing line to achieve an optimal knee joint load distribution. While this method is accessible, it may not fully consider the complexity of individual dynamic knee-loading profiles. This review aims to disclose existing alternative HTO planning methods that do not follow Fujisawa's standard. Methods PubMed, Web of Science and CENTRAL databases were screened, focusing on HTO research in combination with alternative planning approaches. Results Eight out of 828 studies were included, with seven simulation studies based on finite element analysis and multi-body dynamics. The planning approaches incorporated gradual degrees of realignment parameters (weight-bearing line shift, medial proximal tibial angle, hip-knee-ankle, knee joint line orientation), simulating their effect on knee kinematics, contact force/stress, Von Mises and shear stress. Two studies proposed implementing individual correction magnitudes derived from preoperatively predicted knee adduction moments. Conclusion Most planning methods depend on static alignment assessments, neglecting an adequate loading-depending profile. They are confined to their conceptual phases, making the associated planning methods unviable for current clinical use.

Development of a Hybrid SCADA Model Integrating Data Analytics and TSA’s for Effective Linear & Non-Linear Loads in Micro & Nano-grids using nano-technological concept

The rapid evolution of industrial and communication sectors has necessitated the integration of advanced data-driven techniques to enhance the functionality and resilience of Supervisory Control and Data Acquisition (SCADA) systems. This research focuses on the development of a hybrid SCADA model that incorporates Data Analytics (DA) and Time Series Analysis (TSA) to address critical challenges in effective load forecasting, data security, and operational efficiency in micro and nano-grids. Leveraging non-linear methods, the model aims to optimize performance across science, engineering, and communication sectors. SCADA systems play a pivotal role in monitoring and controlling industrial processes, but their vulnerability to cybersecurity threats, especially Distributed Denial of Service (DDoS) attacks, poses significant risks. This study introduces a robust framework that integrates TSA techniques for real-time anomaly detection and predictive modeling to mitigate such risks. By analyzing historical and real-time data using advanced non-linear algorithms, the system effectively forecasts load patterns, identifies potential threats, and enhances the overall decision-making process. In the context of micro and nano-grids, the proposed hybrid SCADA model addresses the unique challenges of these decentralized energy systems, such as fluctuating energy demands, integration of renewable energy sources, and maintaining grid stability. The research employs machine learning-driven data analytics to predict energy consumption patterns, optimize load distribution, and reduce energy wastage. Additionally, the model enhances communication sector applications by ensuring secure data transmission and real-time monitoring through TSA and non-linear analytical methods. The study demonstrates the model's applicability through simulations and case studies across multiple domains. Results show significant improvements in load forecasting accuracy, early threat detection, and system resilience. This hybrid approach bridges the gap between traditional SCADA systems and the growing demands of modern infrastructure, offering a scalable, efficient, and secure solution for the future. Finally, this research provides a comprehensive framework for enhancing SCADA systems by integrating data analytics and TSA, addressing challenges in load forecasting and security, and extending its application to emerging fields in science, engineering, and communication sectors. The proposed model is a step toward smarter, more resilient, and adaptive SCADA systems, particularly in micro and nano-grid environments.

IoT Enabled Intelligent Load Balancing System for Railway Wagons through Artificial Intelligence

This research paper presents an innovative system that integrates IoT (Internet of Things) and AI (Artificial Intelligence) technologies to address the critical issue of load balancing in railway wagons. Traditional systems focus on post-event detection of overloading or under loading, which poses safety risks and inefficiencies. In contrast, our solution leverages IoT-enabled load cells to continuously monitor wagon loads in real-time and utilizes AI-driven algorithms such as Genetic Algorithms and Reinforcement Learning—to optimize load distribution dynamically. The system ensures balanced loading across wagons, preventing overloading and underutilization, improving safety, fuel efficiency, and operational effectiveness. The IoT sensors provide real-time data that is processed by AI models to predict and balance load allocation, thus minimizing the risk of infrastructural damage and enhancing wagon capacity utilization. Initial testing shows a 95%reduction in load imbalances, a 20% decrease in loading time, and improved fuel economy by 10%. This research contributes to railway freight logistics by offering an AI- powered solution for intelligent load management, ensuring safety, efficiency, and cost-effectiveness.

Методи та засоби оптимізації використання обчислювальних ресурсів в корпоративній мережі закладу вищої освіти

The paper considers the problems of using computing resources in the corporate network of a higher education institution. An approach to assessing the efficiency of the load of computer workstations is proposed, which is based on the indicators of the load of individual computing resources and their implementation in the general system of computing resources for individual applied resource-intensive tasks. It is established that to minimize costs when building a university CM, it is necessary to use non-standard approaches that could dynamically distribute computing resources, in particular terminal and cluster technologies for building corporate networks. A number of experimental studies have been conducted based on data on the functioning of the computer network of a higher education institution. A mathematical model of optimal load distribution on terminal servers has been constructed, which describes the dependence between individual network nodes and the file access system.

High Availability Database Infrastructure with Galera and HAProxy

High Availability (HA) is crucial in the cloud environment to render the services according to the Service Level Agreement (SLA) to the customers. This paper provides an novel solution for 99.99% high availability and scalability of database infrastructures using Galera Cluster, along with HAProxy. The Galera Cluster delivers synchronous multi-master replication over multiple nodes, guaranteed data consistency while in real-time and for each transaction, high availability through automatic failover. HAProxy strikes a balance here by distributing the traffic across nodes and achieving optimal load distribution with less or minor effect on downtime. Real-time monitoring and alerts are also enabled by using Prometheus & Grafana to monitor cluster health as well performance.

Sustainability in bridge design—investigation of the potential of topology optimization and additive manufacturing on a model scale

AbstractBridge design represents a typical challenge in civil engineering. The high material usage in combination with the considerable construction effort makes their longevity a strongly desirable property. A mechanically efficient design contributes to optimum load distribution within the structure. Numerical topology optimization represents a valuable tool that can be applied for form finding of load‐appropriate structural layouts within a predefined design‐space. This study takes application of topology optimization to bridge design into focus. Different bridge types are designed using topology optimization considering self‐weight based on a routine that was presented in a previous work. The bridge designs are examined in detail and compared to already existing bridges with regard to the requirements of modern structures. In order to demonstrate manufacturability of the designs, they are realized on a model scale using an additive manufacturing (am) technique in the powder bed. The knowledge gained from this small scale consideration is transferred to the context of civil engineering to highlight the potential of AM in realization of resource efficient construction.

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...

Particle Swarm Optimization for multi-chiller system: Capacity configuration and load distribution

This study applies Particle Swarm Optimization (PSO) to enhance the energy efficiency of a multi-chiller system in a large office building, with a focus on optimizing capacity configuration and load distribution. Given the rising demand for sustainable energy solutions in buildings, where HVAC systems, particularly chillers, account for a significant portion of energy consumption, this research aims to reduce energy use and improve system performance. EnergyPlus simulations, based on Baltimore weather data and a reference large office model, were conducted to build a baseline dataset. This dataset was then used in Python to calculate energy consumption and optimize load distribution and capacity configuration. PSO was applied in three stages: optimizing capacity configuration using five built-in EnergyPlus algorithms, optimizing load distribution, and simultaneously optimizing both. The results showed that optimizing capacity configuration alone improved performance, even using traditional load distribution methods. Load distribution optimization outperformed other algorithms and converged at a 0.7 Part Load Ratio (PLR) for staging. The integrated PSO application achieved an 11.3 % reduction in energy consumption, a 21.5 % improvement in Coefficient of Performance (COP), and a 12.8 % increase in the Seasonal Energy Efficiency Ratio (SEER) by precise operation of 15 different combinations throughout the entire load range. These results demonstrate the potential of PSO to significantly enhance the efficiency of multi-chiller systems, providing a novel approach to optimizing both capacity configuration and load distribution. This research contributes to a robust framework for improving energy performance in building systems and offers valuable insights for future sustainable energy solutions.

Optimal load distribution control for airport terminal chiller units based on deep reinforcement learning

The building sector consumes substantial energy, with HVAC systems accounting for nearly half of the total energy use. Optimizing chiller unit operations is crucial for reducing energy consumption. This research introduces a novel model-free control method for optimizing central chiller load distribution using deep reinforcement learning (DRL). The aim is to address the challenges posed by the high-dimensional complexity and parameter tuning in traditional meta-heuristic algorithms. The proposed method leverages deep neural networks combined with reinforcement learning to optimize chiller control based on real-world data. Case studies conducted in a large airport terminal demonstrate that this approach achieves a 7.71 % energy savings, closely matching the performance of model-based control methods with only a 0.26 % difference. The results indicate that the DRL method not only outperforms traditional expert control systems but also offers a feasible alternative for optimal control in complex, variable environments with limited data. This study contributes to the field by filling a research gap in applying DRL for optimal chiller load (OCL) control and demonstrates its potential for large-scale public building applications. The novelty of this research lies in its model-free approach, which provides a robust solution for energy optimization in dynamic and uncertain environments.

Synergistic approach to wear rate forecasting in AZ31/5% Yttria stabilized zirconia reinforced composite through response surface methodology-genetic algorithm integration

In this work, the wear behavior of a novel AZ31 magnesium alloy reinforced with 5% yttria-stabilized zirconia (YSZ) composite was evaluated using a hybrid Response Surface Methodology (RSM) and Genetic Algorithm (GA) approach. The composite was fabricated using ultrasonic-assisted stir squeeze casting technique, ensuring homogeneous distribution of spherical YSZ particles, as validated by the scanning electron microscope integrated with energy dispersive spectroscope. Wear tests were carried out according to the ASTM standards using a pin-on-disc (POD) tribometer, with applied load (AL), sliding speed (SS), and sliding distance (SD) as the main parameters. An empirical wear rate regression model was developed using RSM/Box-behnken design, and Genetic Algorithm was deployed for parametric optimization, achieving a minimal wear rate of 0.0144 g/m under a load of 30 N, sliding speed of 260 rpm, and sliding distance of 400 m. Confirmation tests were performed to validate the GA predictions. The wear mechanisms were observed, showing reduced wear in GA-optimized samples due to optimized load distribution resulting minimized ploughing, grooving and delamination. This work highlights the efficacy of the hybridized RSM / GA for the wear performance in advanced magnesium alloy matrix composites.

An optimal load distribution and real-time control strategy for integrated energy system based on nonlinear model predictive control

Efficient operational scheduling and control are vital for the operation process of the Integrated Energy System (IES), especially under varying operational conditions. An optimal load distribution and real-time control strategy for IES based on Nonlinear Model Predictive Control has been developed to synergistically improve equipment output performance and reduce operating costs. In the proposed strategy, the IES model that integrates linear parameter-varying models for devices and dynamic models for pipe networks, has been constructed via system identification methodology under varying operational conditions. Thus, a hierarchical operation and control framework is established using the model predictive control strategy, including real-time prediction objective, optimal load distribution objective, and optimization algorithms. Consequently, a case study is performed to investigate the supply-demand balance and economic benefit of the proposed strategy. From the aspect of supply-demand balance, the average energy supply-demand discrepancy by using the proposed strategy (for cooling and heating) is approximately 25 %∼33 % that under the steady-state strategy. By means of the proposed strategy, an outstanding equipment output performance will be achieved and supply-demand discrepancy can be dramatically reduced. From the perspective of economic performance, the proposed strategy, which fully considers the nonlinearity of devices, exhibits a 16 % decrease in the operational cost compared to that under the linear dynamic strategy. Through the proposed strategy, efficiency losses are expected to degrade with an excellent economic benefit. Therefore, the suggested strategy is promising and suitable for varying operational condition scenarios.

Influence of bioinspired morphing on the flow field characteristics of UAV wings at low Reynolds number

The aerodynamic performance of unmanned aerial vehicles (UAV) can be improved by optimizing the surface flow characteristics over a wide range of angle of attack (AoA) through novel mechanisms. Recently, the bioinspired camber morphing concept has received greater attention because of the proven ability of nature species towards the retention of aerodynamic performance under different environmental conditions. In particular, birds like Eagles ( Accipitriformes) increase their wing camber in the course of flight to achieve maximum climbing altitude with good manoeuvring capability. The biomimetic designs such as the corrugated bone structure of Eel fish ( Anguilliformes) helps to achieve the wing camber morphing with optimal aerodynamic load distributions. The present work is focused on the bioinspired variable camber morphing (VCM) strategy to enhance the flow control behaviour and aerodynamic forces for a specific UAV wing configuration at various AoA. Here, NACA 4412 airfoil is used as a baseline wing configuration and the camber morphing mechanisms which are derived through Eel fish and Eagle are analysed. The model with Eagle wing morphing (EWM) mechanism is considered as a primary case of VCM and Eel fish’s corrugated structure is taken as a secondary case of VCM model. The coefficient of lift ( C L ), coefficient of drag ( C D), coefficient of pressure ( C p) and endurance factor are estimated for both morphed and baseline wing configurations through high fidelity numerical simulations. Interestingly, it is observed that the EWM wing configuration has excellent surface flow control characteristics than the CSM wing configuration and the results are presented with a detailed discussion.

Optimal scheduling of virtual power plants considering integrated demand response

Abstract In contemporary power system management, leveraging multilevel demand response strategies plays a pivotal role in balancing energy supply with demand, thereby enhancing system efficiency and flexibility. This paper introduces an innovative optimization model for virtual power plant operations that anticipates the scheduling needs several days in advance. It incorporates a comprehensive approach to demand response on the consumer side, highlighting two main types: price-based and substitution-based responses. Furthermore, the model integrates the functionalities of energy storage and conversion devices. This integration aims to facilitate effective load management, ensuring an optimized load distribution while minimizing the overall system costs.

Optimum management of microgrid generation containing distributed generation sources and energy storage devices by considering uncertainties

In recent years, the financial and energy crises have made the economical and safe operation of distribution microgrids one of the main challenges for operators. In this regard, the optimal distribution of the microgrid, considering the favorable indicators of Generation and consumption, significantly improves the quality of the development and economic operation of electricity grids. On the other hand, distribution microgrids have many problems and complications due to the presence of DG sources and the uncertainties caused by them in Generation and consumption, such as the inability to solve optimal load distribution using common and common calculation methods. Therefore, grid technical indicators such as voltage deviation and losses in solving load distribution add to the complexity of related issues. To solve the challenges raised in the distribution microgrid, this paper proposes an optimal load distribution model that models the uncertainties in the distribution microgrids using the Monte Carlo method. Using meta-heuristic algorithms, the optimal Generation function by modeling grid security constraints such as voltage deviation, losses, and a load limit of the system should be in economic operation mode, and then the power distribution equations should be solved. The battery can supply 1 kW of active power to the system at a cost of 1 unit/kW during the high cost period if it absorbs 1 kW of active power at a cost of 0.92 units/kW during the low cost period. Additionally, the active and reactive power losses have been decreased to 0.3kW and 0.2kW, respectively, by utilizing the suggested methodology. The simulation results show that the proposed method performs satisfactorily and that the modeling is simulated in MATLAB software.

Systematic Review: Particle Swarm Optimization (PSO) based Load Balancing for Cloud Computing

In this modern era cloud computing and widespread use of it have led to a massive spike in users' requests. The infrastructure's resources would either not be able to handle the increased volume of traffic or the scenario would lead to some resources being over or underutilized. Due to the unpredictable spike in traffic the system’s performance eventually grades. An optimal load distribution is therefore the area of concern for controlling traffic so that all available resources are fully used if possible in order to increase system efficiency. Static, heuristics-based, traditional dynamic and metaheuristic-based algorithms are ways that have been used for effective load balancing. The issue is challenging since there isn't a single best fit approach. The objective of this systematic review is to study the utilization of Particle Swarm Optimization method along with its proposed variations to distribute the incoming traffic evenly with efficient resource utilization.

Performance optimization and energy minimization of cloud data center using optimal switching and load distribution model

Cloud computing is an effective computing methodology used in all stages of business. Most of the Cloud Data Centers (CDC) operates on the basis of peak load and huge scales. Hence, it necessitates saving the energy in CDC. This study introduces an energy-efficient strategy based on the fat tree. Here, Taylor-based Manta-Ray Foraging Optimization (Taylor-MRFO) is developed by combining the Taylor series with Manta Ray Foraging Optimization (MRFO) to distribute the load in a CDC. In load distribution, the cloud data switching to the preferred mode is done by the Actor critic neural network (ACNN). Furthermore, the developed Taylor-MRFO+ACNN provided a better outcome than the conventional approaches with the least energy consumption of 0.4930, least load of 0.3631, and least fitness of 0.4343. For setup-1, when the population size is 15, the load value obtained by the proposed method is 23.43 %, 10.19 %, 7.18 %, 5.31 %, 4.43 %, and 2.58 % higher when compared to the existing approaches namely, Artificial Bee colony(ABC), Efficient Load Optimization and Resource Minimization (ELORM), Adaptive Parameter- Ant Colony Optimization (AP-ACO), Multi-Objective Memetic Algorithm-Adaptive Plant Intelligent Behavior Optimization (MOMA-APIBO), Cooling Control Algorithm (CCA), and Minimum Total Power (MinPR).

Towards a new frontier in wrist rehabilitation: The traction-free posture orthosis

This study presents the novel Traction-Free Posture Orthosis, designed to address wrist stiffness without the use of traditional traction methods. The orthosis employs a velcro system to stabilize the position of the wrist in flexion or extension, thereby avoiding the compression and rotational disadvantages associated with non-perpendicular traction forces. Triangular-shaped cuts in the brace allow for plastic material flexibility, ensuring movement and comfort. Significant is the absence of a traction strap, which traditionally reduces the support surface and could create pressure points leading to patient discomfort or treatment rejection. The design facilitates a wide contact surface with the hand, optimal load distribution, and the capacity to adjust dual traction straps independently, adapting to the natural wrist movement. This single-piece base orthosis offers a stable, cost-effective, and patient-friendly alternative to conventional splints. However, patients with stiffness in both flexion and extension may require two separate orthoses, potentially increasing costs and patient inconvenience. Overall, the Traction-Free Posture Orthosis represents an innovative step in wrist rehabilitation, providing a quick-to-construct, easy-to-wear solution with the promise of enhanced patient compliance and comfort.

FE-analytical slice model and verification test for load distribution analysis and optimization of planetary gear train in wind turbine

Load distribution behavior of planetary gear train (PGT) is crucial for the contact and bending fatigue failure of wind turbine transmission system. With the increasing power of wind turbines, the load and failure rate of wind turbine become larger, which leads to the demand of accurate and efficient modeling, analysis and optimization for load distribution of PGT. In this paper, a new efficient and accurate FE-analytical slice model for load distribution analysis and optimization of PGT in wind turbine is proposed, which is explicitly formulated by the geometry slice model of external/internal gear contact, original FE-analytical slice model for local contact deformation, and efficient finite element (FE) model for global structural deformation. In the FE-analytical slice model, geometry errors are effectively descripted by discretizing gears into a series of slices, while gear tooth contact elasticity and structural elasticity are addressed by the FE-analytical method accurately and efficiently. Upon the proposed model, the load distribution behaviors of PGT with coupling effects of elasticity and errors are revealed and optimized by tooth modification and in-phase eccentricity arrangement strategy. The load distribution tests of PGT in wind turbine are conducted, verifying the proposed FE-analytical slice model which is significant to improve the load-bearing capacity and avoid the fatigue failure of wind turbine transmission system.