Search

Generally, the distributed energy storage systems (DES) can be defined as a set of small size of storage energy systems that allocated on the electrical distribution network and more specifically at the customer building side or the distribution substation. Indeed, the DES is expected to be sorely needed and with penetration, levels exceed the DG units in the future, as the DES associated with a significant number of benefits in terms of supporting renewable energy resources, improving power quality and reliability as well as managing the produced energy and the required demand. Energy storage systems provide several benefits in terms of assisting utilities toward achieving their goals. One of these benefits is improving the reliability of the system through effective islanding operation. Shortly, the reliability of the distribution grids is considered as an important objective with DG and DS units that need further investigation and research with considering the effective approach. Therefore, this research focuses on finding the optimal energy storage units location with the amount of load that need to be shed to improve the overall reliability of these systems through using particle swarm optimization technique (PSO). The general method that will be used in this research includes two phases, which will be solved and analyzed through using particle swarm optimization method (PSO) in order to reduce the objective function of this research. The first phase will perform load flow to calculate power requirement for energy storage will the second phase will apply a sequential Monte Carlo simulation (MCS) to the whole system to check the operation mode of the system. A 33-bus radial test system was used to in this study with four different storage systems that compared with the base case of without any DS units allocated in the test system. [ABSTRACT FROM AUTHOR]

Research has demonstrated that Machine-Learning Phishing Webpage Detectors, which is vulnerable to malicious changes of the input webpage's HTML code. Unfortunately, the newly suggested assaults have not been very effective so far because they target specific HTML components and don't optimise the employment of the manipulations that have been accepted. To get over these limitations, we created a novel set of granular manipulations that can change input phishing webpage HTML code while keeping its aesthetic appeal and malevolent intent (alterations are rendered-preserving and functional by design). After that, we use a query-efficient black-box optimization technique to choose which alterations to apply in order to evade the target detector. In contrast to the weaker attacks created in earlier work, our results demonstrate that our attacks may completely demolish the performance of state-of-the-art ML-PWD with only 30 questions. This allows for a considerably more equitable evaluation of ML-robustness. PWD's [ABSTRACT FROM AUTHOR]

The article deals with the problem of rehabilitation of patients with impaired motor function using robotic systems for rehabilitation. Several systems for lower limb rehabilitation based on parallel structure mechanisms are considered. A number of disadvantages for some mechanisms are revealed. A synthesized hybrid parallel-serial structure mechanism for lower limb rehabilitation was considered. Optimization of parameters with the use of the developed program in C++ language was carried out. As a result of optimization of the active mechanism parameters, a set of Pareto optimal solutions for the parameters corresponding to five different configurations of the active mechanism dimensions was obtained. In the course of simulation modeling, the calculated characteristics of the mechanism have been verified. The results of mathematical and simulation modeling made it possible to reveal a number of inaccuracies at the stage of design and fabrication of the hybrid mechanism prototype, related to the intersection of links and mutual arrangement of mechanisms, which allowed to make timely changes in the appropriate attachment nodes and joints. An experimental sample of a hybrid mechanism for lower limb rehabilitation was developed for practicing various rehabilitation procedures in medicine and conducting effective mechanotherapy. [ABSTRACT FROM AUTHOR]

Urban regions have serious concerns with solid waste management. To manage solid waste,there can be various kinds of obstacles. These include urbanization, population increase, and the requirement for sustainable trash disposal. The necessity to effectively manage the growing amount of municipal solid waste (MSW) in major metropolitan areas is one of the most important environmental challenges to deal with. Even with huge attempts to build strong solid waste management systems, current approaches sometimes fail to offer long-term ways to lower the amount of solid waste produced. In order to evaluate solid waste generation in large metropolitan settings, this research presents an analytical methodology review that combines geographic information system (GIS) methodologies with statistical and numerical optimization techniques. The technique was successfully used to estimate the production of municipal solid waste (MSW) in different areas of Nagpur. The findings of the analysis of the solid waste generation data, broken down by the socioeconomic standing of each collecting region, are offered. By classifying various strata according to their purchasing power, these socioeconomic levels may be understood. The results specified in review demonstrate the usefulness of these GIS and numerical optimization methodologies as a helpful supplemental tool for the research and design of efficient and sustainable solid waste management systems. [ABSTRACT FROM AUTHOR]

The research paper was aimed to optimize the routes of transporting yogurt milk from Annashuwa Company, Sharada Phase II Kano, to expedite the distribution route led to the reduction of operating cost that frequently occurs due to the traffic congestion in some selected areas within Kano city, Nigeria. To reduce the cost of transportation from the company to the destinations. A Mathematical Optimization Model i.e., Vogel approximation was employed to ease the cost of distributing the product to some selected destinations in the state. [ABSTRACT FROM AUTHOR]

This research evaluates the use of metaheuristic optimization techniques such as the Ant Lion Optimizer (ALO), Bat Algorithm (BAT), and Particle Swarm Optimization (PSO) for economic dispatch solutions. Using a 26-bus IEEE standard system as a benchmark, the effectiveness of these methods is assessed in a dynamic economic context. All methods were modeled in MATLAB. Applying two distinct case studies to the 26-bus system, the results reveal that ALO provides an optimal balance between fuel costs (FC) and transmission line losses. In contrast, the PSO method was the least effective, exhibiting both longer processing time and higher fuel costs. Notably, the ALO and BAT techniques demonstrated superior performance regarding transmission line losses as opposed to the traditional PSO. [ABSTRACT FROM AUTHOR]

Installing distributed generation units can yield numerous benefits in meeting power requirements and enhancing effectiveness. To fully capitalize on these advantages, it is crucial to strategically deploy the Distributed Generation (DG) units in optimal locations. Alternatively, installing this equipment could negatively impact the energy quality and system operation. Various optimization strategies have been developed to enhance the integration of distributed generating. Optimization techniques are subject to ongoing evolution and have recently been the primary focus of numerous innovative research endeavors. This article assesses advanced methods for optimizing the placement and sizes of DG units powered by renewable energy sources. The evaluation is based on current research publications that have already been implemented to optimize distribution systems. Additionally, this article highlighted the environmental, economic, technological, and regulatory factors that have contributed to a significant increase in interest in the DG system powered by renewable sources. The table summarizes widely used meta-heuristic optimization tools, including their advantages and disadvantages. This aims to explore new potential approaches that have not been thoroughly investigated. [ABSTRACT FROM AUTHOR]

Ensuring precise representation of solar cells is pivotal for optimizing solar photovoltaic (PV) system performance. Presently, existing solar cell models lack accuracy due to unattainable parameter data, rendering manufacturer datasheets insufficient for reliable PV cell modeling. Hence, the accurate estimation of requisite parameters becomes imperative for attaining precise solar cell models. To address this, this study introduces a straightforward multi-objective optimization algorithm to derive cell parameters. The approach acknowledges that prevailing algorithms often yield suboptimal outcomes due to local minima and untimely convergence. The primary aim is to establish the proposed algorithm's reliability. Numerous optimization methods have emerged to tackle this concern; however, their efficacy frequently falls short, resulting in undependable outcomes. Consequently, the suggested algorithm is benchmarked against established optimization techniques to showcase its competence. Empirical findings, coupled with statistical scrutiny, corroborate the algorithm's performance relative to parameter estimation. Furthermore, outcomes underscore the suitability of the proposed method for precise solar PV modeling. The algorithm's uncomplicated structure and heightened accuracy underscore its versatility for diverse applications within the solar energy domain. Additionally, its computational efficiency and ease of implementation enhance its value as a valuable asset in solar energy research. In conclusion, the proposed algorithm furnishes a meticulous and dependable avenue for parameter estimation indispensable in solar cell modeling, thereby enhancing overall solar PV system efficacy. [ABSTRACT FROM AUTHOR]

Traditional file systems, while fundamental in data management, often grapple with the demands of ever-evolving data workloads, varying access patterns, and escalating volumes of information. In response to these challenges, the integration of artificial intelligence (AI) and machine learning has emerged as a transformative approach to optimize file systems. This paper outlines the importance of file system optimisation in the contemporary data landscape and then provides potential solutions using AI algorithms. AI-driven file systems optimization has delivered substantial performance improvements, characterized by enhanced Input/Output Operations Per Second (IOPS), reduced latency, and increased throughput. Artificial intelligence-driven solutions in cloud storage environments address scalability and data management challenges associated with cloud storage, extending the applicability of AI techniques. [ABSTRACT FROM AUTHOR]

Queue activity is better modeled with stochastic techniques. This technique is a step to achieve the goal, which is to assess the utility of the teller and the effectiveness of the most efficient service value. This research method uses stochastics on Simulink with 1000 and 10000 experiments. This modeling is shown with graphs to facilitate analysis. The study showed that the highest utility was 40% and effectiveness per service was 5.04 minutes on Teller server 1. However, Teller on other servers can still be controlled if created with different simulation modeling. However, we can provide a basic reference that the utility value of each server is determined by the value of the service provided to the customer. [ABSTRACT FROM AUTHOR]

Increasing load in a traditional transmission system has led to the increase of current flow which also increases the I2R loss leading to monetary losses in the power system community. This will also affect the economic status of the utility which may require the utility to take possible appropriate remedial action. Thus, a compensation process can be performed to alleviate the high current flow which in turn reduces the monetary losses that can be experienced by the utility. This paper presents Multi Load Variability for Composite Compensation in Loss Control Scheme. In this study a new optimization technique termed Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP) is proposed to optimize the location and sizing for composite compensation in power transmission system for loss control scheme. Several variations in the loading variability were conducted to evaluate the effect of reactive power changes which influence loss value in power system. The effectiveness of the proposed ICAMEP algorithm was compared with respect to the traditional evolutionary programming (EP) and artificial immune system (AIS) optimization techniques for performance comparisons. Results from ICAMEP outperformed AIS and EP in terms of achieving lower transmission loss through the validation process in the IEEE 30-Bus Reliability Test System (RTS). This will benefit power system operators and planners for their expansion planning efforts. [ABSTRACT FROM AUTHOR]

This article proposes a multi-objective Integrated Immune Moth Flame Evolutionary Programming (MO-IIMFEP) algorithm to identify the optimal sizing and placement of distribution generation (DG) in a radial distribution system (RDS). These objectives are simultaneously minimizing the total active power loss, reducing the total operating cost and reducing the cumulative voltage deviation (CVD) while considering the distribution system's operational constraints. With the aid of the fuzzy decision-making procedure, the non-dominant Pareto solutions are narrowed down to the optimal prospective compromise solution. The proposed efficacy is evaluated using a bulk distribution system, i.e. IEEE 118-bus RDS, and the outcomes are contrasted with multi-objective Evolutionary Programming (MO-EP) and multi-objective Moth Flame Optimization (MO-MFO) approaches. The outcomes demonstrate that the MO-IIMFEP algorithm is effective in obtaining the best compromise solutions for multi-objective problems. The study also shows that installing DG Type 1 into a distribution system with multi-objective optimization substantially reduces total power loss, enhances cumulative voltage deviation, and minimizes the total operating costs. [ABSTRACT FROM AUTHOR]

Poor power quality is a major concern in power system, which can lead to equipment damage, power interruptions, and safety hazards. In modern days, due to non-linear loads like UPS, inverters, converters, etc., the supply current takes on a non-sinusoidal shape. Multilevel inverters are widely used in power systems to improve power quality in higher voltage levels with fewer switches and by reducing harmonic distortion which improves the overall efficiency of the system. Several optimization techniques can be used to design and control multilevel inverters for improved power quality performance. The important object of this review paper is to identify various optimization techniques while taking various power quality-related challenges into account. Thus, the current study analyses different power quality issues and their improvement with multilevel inverters. The paper also discusses overviews of various technical and common issues presented in the literature, all carried out in a comprehensive manner. Finally, this paper offers a scenario of the current state of power quality improvement along with different optimization techniques which discovers research gaps along with suggestions for future research directions. [ABSTRACT FROM AUTHOR]

Improving object recognition in computer vision by combining the Whale Optimization Algorithm (WOA) with Seagull Optimization Algorithm (SOA), two nature-inspired metaheuristic optimization techniques. Object detection plays a critical role in automated object recognition and localization in images and videos. The ResNet architecture has established its effectiveness in learning deep representations, while convolutional neural networks (CNNs) have demonstrated exceptional object detection performance. Both the WOA and the Seagull Algorithm are well-known for their unique exploration and exploitation skills, inspired by natural actions. By leveraging the complementary capabilities of these algorithms, the proposed approach aims to enhance the accuracy and efficiency of object identification in deep learning models. The WOA specializes in global exploration and convergence, mimicking the hunting thoughts of humpback whales. Meanwhile, the Seagull Algorithm simulates the swarming behavior of seagulls, focusing on local search and exploitation. Extensive studies on popular object detection are carried out to validate the efficacy of the suggested technique benchmarks, including KITTI, COCO, CUSTOM and PASCAL VOC datasets. The results demonstrate that the fusion of the Seagull and Whale Optimization Algorithm with the ResNet model consistently outperforms traditional optimization techniques and baseline object detection methods. The algorithm exhibits improved accuracy, robustness, and generalization on various challenging scenarios, showcasing its potential for real-world applications. [ABSTRACT FROM AUTHOR]

The implementation of two optimization methodologies, Biharmonic and Thin Plate Spline, aims to improve the cut-off frequency of InAlGaN HEMT on SiC substrate. This is achieved by altering the aspect ratio of the gate length/width to 0.15nm. The study consists of two groups: Group 1 using Biharmonic optimization (BHO) and Group 2 using Thin Plate Spline optimization (TPSO). Each group has 7 samples, resulting in a total of 14 samples. The statistical analysis shows that there is enough statistical power (G power=0.84) to draw meaningful conclusions. The objective is to enhance the cut-off frequency by utilizing AI-driven optimization to adjust the gate length in HEMT. The SPSS analysis of the independent sample t-test shows a result that is not statistically significant (p>0.05) with a value of 0.216. The Biharmonic optimization obtains a cut-off frequency of 242 GHz, while the Thin Plate Spline optimization achieves 209 GHz within the gate length/width range of 0-35 nm. The highest cutoff frequency is observed at a temperature of 35nm, reaching a value of 242.24 GHz. This study highlights the improved performance of Biharmonic optimization (242 GHz) compared to Thin Plate Spline optimization (209 GHz) in improving the cut-off frequency of InAlGaN HEMT for various gate lengths, underlining its potential for power electronics devices. [ABSTRACT FROM AUTHOR]

This idea presents a comparison between the novel Rider Optimization Algorithm (ROA) and the Levy Flight Distribution Algorithm (LFDA) for enhancing the power factor of the power distribution network using optimal placement of Distributed Generation (DG). This study compared two algorithms for optimizing the placement and sizing of distributed generation (DG) units in a radial distribution system: Novel Rider Optimization Algorithm (ROA) and Levy Flight Distribution Algorithm (LFDA). The researchers used Gpower software to determine a sample size of 14 for each group (likely two scenarios with different DG placements/sizings) and set a power analysis value (G power) of 0.8. The results showed that ROA outperformed LFDA in terms of improving the power factor. This improvement was statistically significant based on an independent samples t-test (p-value=0.001, less than 0.05). Specifically, the ROA method achieved a significantly better power factor enhancement (0.8593 pu) compared to the LFDA method (0.8495 pu). (pu refers to per unit, a way to express power factor on a common scale). [ABSTRACT FROM AUTHOR]

Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level. [ABSTRACT FROM AUTHOR]

The study aimed to optimize the replacement levels of table sugar, different types of flour and butter to develop a new healthy and more dietary fiber seaweed butter cookies. The study was carried out using response surface methodology (RSM) as the optimization technique. The butter cookies optimum formula was designed using the following mixing ratio: seaweed flour: wheat flour (g: g), overripe banana sweetener: table sugar (g: g) and butter: margarine (g: g), before performing the sensory evaluation. The physical properties of seaweed flour butter cookies showed a higher thickness but lower in terms of spread ratio, diameter and weight compared to the wheat flour. Seaweed flour butter cookies (SFBC) were harder and different in color as compared to wheat flour butter cookies (WFBC). SFBC had a higher and significantly darker color than the WFBC. Optimization through RSM suggested that the seaweed flour butter cookie (SFBC) was the best formula. Treatment 12 (seaweed flour 4%, OBS 100% and butter 100%) is the optimized formula for the development of a healthy and innovative seaweed butter cookies. [ABSTRACT FROM AUTHOR]

This paper presents the comparison ofselective harmonic elimination in a cascaded h-bridge multilevel (CHBMLI) inverter using PSO and TLBO optimization algorithms. The inverters are employed in a variety of applications, including high-voltage dc transmission, variable drive systems, and electrical vehicles. The inverter's output includes harmonics. PSO and TLBO algorithm is used in this paper To find out the best switching angles for a CHBMLI selective harmonic elimination. In order to get the appropriate fundamental component and get rid of low-order harmonics, this project solves nonlinear equations. These algorithms can produce the most effective triggering pulses to minimize Overall Harmonic Distortion(THD) and Selected Harmonics. If the harmonics are removed, the inverter's output will be improved. MATLAB is used to implement and compared both optimization techniques usingthe seven-level inverter. [ABSTRACT FROM AUTHOR]

Modern day military weapons are an integration of a several sub-systems and components. Each of these systems possesses distinct reliability characteristics, accompanied by diverse probability distributions that dictate their rates of failure. This paper focuses on the design of automated test equipment (ATE) and development of Graphical User Interface (GUI) for verification of weapon's Line Replaceable Units (LRUs) health status during functional ground testing of avionics systems. Functional ground testing of weapon LRUs through ATE is essential for ensuring the reliability and effectiveness of weapon. ATE is designed to perform Intermediate level periodic maintenance activity which includes the in-depth inspections, repairs, and replacements of faulty components. ATE is a computer-controlled system designed to perform tests automatically, eliminating human error and ensuring consistent results. It can perform a variety of tests, including electrical, functional, and environmental testing. A GUI is developed using Qt and C++ for verification of tests. The software supports multiple LRUs testing as well as individual LRU testing. The ethernet communication protocol between ATE and Unit Under Test (UUT)can be used to test multiple units at a time. During the functional ground testing process, the weapon is connected to the ATE and then ATE will send a series of signals to the weapon, simulating various operational scenarios. The ATE will monitor the weapon's LRU's response, comparing it to the expected results. If the LRU's response is outside the expected range, the ATE will flag the specific unit as faulty, and it will be removed from service. ATE will generate the test report of health of the constituent components. ATE provides a reliable and efficient way of identifying any issues with LRUs in the pre-flight phase and reduces the risk of malfunction. The use of ATE can increase testing efficiency and reduce costs, making it a valuable tool in modern warfare. [ABSTRACT FROM AUTHOR]

Rapid Since it has strong relations to video analysis and image interpretation, object detection has attracted a lot more academic attention in recent years. Conventional object recognition methods are made easier by shallow trainable structures and handcrafted features. Complex ensembles that combine high-level data from object detectors and scene classifiers with suboptimal visual elements cross an effectiveness threshold reasonably rapidly. As deep learning advances rapidly, more potent tools that can learn deeper, higher-level, and more semantic characteristics are becoming available to assist with issues with conventional architectures. These models, for instance, act differently when it comes to network design, training methods, and optimization techniques. In this article, we review research on deep learning-based object recognition. The study's writers begin with an introduction to deep learning and its main techniques, the convolutional neural network (CNN). The focus will then switch to several popular generic object recognition techniques, as well as various advancements and useful tactics for enhancing detection more generally. The subject of various generic object identification patterns will then be discussed, along with some modifications and useful techniques for improving detection. Increased efficiency It also quickly examines a number of specific detection tasks, including the identification of pedestrians, the detection of faces, and the identification of remarkable objects because they exhibit a variety of properties. There are also experimental analyses accessible to contrast alternative tactics and produce some useful information. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enhance microgrid resilience through the application of robust Model Predictive Control (RMPC). The primary objective is to minimize overall cost during prolonged main grid outages by optimizing resource allocation. The proposed methodology encompasses two essential phases: grid connected operation and resilient operation. In the former, optimal unit commitments, energy storage schedules, and adjustable loads are determined to inform the subsequent resilient operation phase. During resilient operation, the microgrid's capacity to fulfill local load demands amidst main grid disruptions is evaluated. A crucial element of this approach is the mitigation of uncertainties through robust optimization techniques, thereby guaranteeing the solution's resilience in the face of variations in load, non-dispatchable generation, and the timing of main grid interruptions. The resulting refined solution strikes a delicate equilibrium between economic efficiency and resilience, thereby facilitating seamless islanding with minimal disruptions. Through numerical simulations conducted within a test microgrid, this approach's effectiveness is validated, highlighting its potential to bolster microgrid resilience by strategically optimizing resource allocation, particularly in critical scenarios. [ABSTRACT FROM AUTHOR]

Energy consumption and network lifetime are significant issues for a WSN. Numerous optimization techniques can be employed to select a path between nodes at source and destination. The challenge is in establishing a routing algorithm which takes into account the crucial factors of reducing the energy use and enhancing network longevity. This study article introduces a broad categorization of WSN optimization methods for energy-aware routing. This review's objective is to present several decentralized energy-aware routing strategies that are appropriate for most WSN applications that demand reducing energy use and improving network lifetime. The study is then concluded with a comparative analysis that highlights the key problems and possible future paths. [ABSTRACT FROM AUTHOR]

Fossil fuels are depleting at a significantly higher rate, global warming reaching its new high and entire ecosystem suffering from air pollution. Biodiesel has emerged as potential substitute of diesel fuel that can be operated in a conventional diesel engine without any sort of major modification and provides comparable performance and lower emissions. One of the cumbersome tasks is to find the optimum blend of diesel-biodiesel and engine operating parameters. In this regard, many optimization and simulation methods have been used to optimize the biodiesel percentage in blend and engine operating parameters, which gives maximum power output and minimum emissions. There are several optimization techniques such as Genetic algorithm, Ant colony optimization, Grey relational analysis, Response surface methodology, Taguchi, artificial neural network, VIKOR and TOPSIS, which have been employed to optimize the working of a diesel engine run on biodiesel blends. In this research paper, the various optimization methods that have been used in a diesel engine for predicting & optimizing the several output parameters of the input model and ranking them accordingly are discussed. [ABSTRACT FROM AUTHOR]

We approach the topic of improving the dependability of complex System As a multi-objective fuzzy optimization problem, system dependability, cost, volume, and weight are also included, are all considered fuzzy objectives. Using practical examples, our study computes reliability optimizations for complex systems with cost and weight constraints. [ABSTRACT FROM AUTHOR]

Issues of taking into account functional restrictions in the form of inequalities when optimizing the modes of electric power systems based on the optimization algorithm described above require separate research. This is due to the fact that under certain conditions for the implementation of conditionally optimal plans obtained using the described algorithm - based on identifying certain values of the initial parameter in a given interval and solving deterministic optimization problems, some functional restrictions in the form of inequalities may be violated and thus such plans may not be valid. [ABSTRACT FROM AUTHOR]

At the present stage of development of the energy industry of the world, in particular the Republic of Uzbekistan, one of the main directions is connected with the processes of introducing the solar photovoltaic systems into them. The design of such systems involves the selection of the optimal composition of their constituent elements, taking into account the conditions of their long-term operation. Despite the existence, at present, of a number of methods for solving such problem, there are no algorithms based on the use of rigorous mathematical models and optimization methods, taking into account all influencing factors. This paper proposes a mathematical model and algorithm for optimization the composition of elements of autonomous solar photovoltaic systems, taking into account limiting conditions. The results of a study of the effectiveness of this model and the calculation algorithm are presented. Using the development allows you to provide high efficiency by improving the accuracy of optimization. [ABSTRACT FROM AUTHOR]

Under-frequency load shedding (UFLS) has been used to restore the electricity system after a major outage. However, based on the research of recent blackouts, voltage collapse and voltage-related issues are also major challenges in preserving system stability. As a result, both frequency and voltage must be considered in load shedding methods. To calculate the quantity of load to be shed and its optimal location, the research considered both aspects when building a load shedding plan. Voltage sensitivity is used to identify the location and amount of load to be shed from each bus. Newton-Raphson load flow method is used considering ZIP load model to show the enhancement in voltage profile and total loss. To test the validity of recommended attitudes, the IEEE 39-bus is used. For accurate results, a gravitational binary search optimization technique (BGSA) is utilized, and comparisons are made to demonstrate the efficacy of the proposed strategy. A BGSA optimization technique can help reduce the search space for the smallest amount of power shed and its location. The results show that the supplied techniques can employ the BGSA optimization algorithm to discover the optimal solution for a significant loss reduction and a better voltage profile and frequency. The planned disruptions include the loss of a generator for several buses and the loss of transmission lines in multiple situations. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of automatic cooling and cleaning system (ACCS) for PV modules to optimize the performance of the PV module based on electrical efficiency drop resulting from increased cell temperature at loading and dust accumulation on PV module s surfaces. An increase in PV module temperature causes a linear reduction in its performance, affecting electrical power generation. Pressurized water jets by nozzles are used to cool and clean the front surface of the PV module. The theoretical model of the PV module is verified by MATLAB/Simulink software. The operational performance of ACCS was recorded and analyzed. The experimental results showed before cooling, the maximum power produced by the PV module was 65.31W, while after cooling increased by 73.24W. Also, electrical efficiency yield of before cooling the rate of decreasing efficiency 0.052% per 1℃ while after cooling the efficiency increased by 0.13% for each decrease 1℃. [ABSTRACT FROM AUTHOR]

The last decades have been showing an explosive development in optimization techniques and implementation of interdisciplinary optimizations in aircraft design within various systems engineering and concurrent engineering analyses. The quality of the optimization calculations depends on availability of large amounts of data describing the modeled processes; hence obtaining fast solutions of dynamic models continues to be of significant interest. Many purely numerical integration methods have been proposed during the last decades and some matured and grown into new practical algorithms; however, limits exists that cannot be exceeded by numerical simulations alone, hence the interest in the (semi)analytical solutions have been re-growing as well. The current paper addresses the ordinary differential equations that governs the longitudinal maneuvers of airplanes and includes both purely numerical integrations and approximate analytical solutions. The analytical solutions are based on linearized equations, some of which are provided by small perturbation lgorithms.. [ABSTRACT FROM AUTHOR]

The use of vibration-based experimental tests usually reveals that structural response of the actual constructions of artefacts may significantly deviate from the expected behaviour foreseen by design procedures. For this reason, several numerical techniques have been developed to update the analytical model. In this research work an optimization procedure, aiming at updating the structural model upon the results of dynamic tests, is proposed and discussed. This approach take advantage of the so-called Continuous Particle Swarm Optimization, an optimization tool recently derived from Particle Swarm Optimization technique. The results of a full-scale shaking table test on an high-voltage electrical equipment in terms of modal parameters have been used to validate the proposed procedure and to assess its robustness. [ABSTRACT FROM AUTHOR]

Thermal power plants provide the majority of the power in an integrated power system. The load patterns vary greatly between peak and off-peak hours. As a result, sufficient energy output is required to fulfill the rising energy demand. The system operator's difficulty is determining which units should be decommissioned and for how long, or in other terms, thermal unit scheduling. As a corollary, the commitment to thermal units is described as committed sufficient units to transmit power to a network or system in the most efficient manner possible depending on load demand. It's a nonlinear mixed-integer optimization technique with several constraints. One strategy is presented for the purpose of resolving the thermal unit commitment issue (TUCP) in this study, out of many conventional and random search techniques. The ideal solution for TUCP with four and ten generating units was obtained using evolutionary programming, and the production cost was found to be lower than using dynamic programming. On the other hand, the calculation time is longer, which is advantageous for large-scale execution. [ABSTRACT FROM AUTHOR]

Generation of very fast transient over voltages (VFTOs) is very common during switching operations in gas insulated substations (GIS) and it is extremely harmful for substation equipment like transformers as it becomes a reason of insulation damage due to its less arising time. Sometimes, the VFTOs magnitude may be very near to the basic insulation level (BIL). Improvement and investigation of such transients in power transformer is of crucial importance in GIS as transformers are vital components of power system network. Generation of transient issues considers the generation and propagation of VFTOs while switching of circuit breakers or disconnector switches or earth faults. In this research, the investigation is on VFTO by putting intelligible models for a GIS of a capacity 400 KV. This paper presents a modeling guideline for VFTO analysis in GIS and, further mitigation techniques such as Nano-crystalline Rings, RC Filter, Ferrite Rings, Damping Resistance etc. have been studied using MATLAB simulation. Using an intelligent optimization technique like PSO, VFTO waveforms can be scaled down further to controllable limits so that substation components can be saved from impending failures. Further VFTO effect on GIS such as stress on substation Transformer leading to winding deformation has been discussed in this paper and localization of deformation and type of deformation in transformer winding is also carried out using intelligent classifier. [ABSTRACT FROM AUTHOR]

Minimization of the cost of generation for any utilities is crucial to ensure the utilities will be able to maintain continuous supply and their survivability. Non-optimal amount of power generated by all generating stations in a country will possibly lead to monetary loss and ineffective operation of the power system. Thus, a robust and reliable optimization technique is the prerequisite to ensuring the lowest cost of generation can be achieved. This paper proposes a hybridized optimization technique that integrates the element of evolutionary programming (EP) into the barnacle mating optimizer (BMO), termed Hybrid Evolutionary Programming-Barnacles Mating Optimization (HEBMO). HEBMO is utilized to address the convex economic dispatch in a power transmission system. Its implementation on the IEEE 30-Bus Reliability Test System (RTS) in addressing the convex ED is remarkable, through the comparison with the traditional EP and BMO. The cost of generations in chosen cases such as base case conditions, stress conditions due to real power, and reactive power increments revealed the superiority of the proposed HEBMO over EP and BMO. [ABSTRACT FROM AUTHOR]

Decomposition of organic matter is sensitive to air flow, humidity and temperature, thus appropriate conditions are needed for effective composting which are achieved through a correct composter design. The paper focuses on a simulation analysis for the injection molding process of the bottom part of a composter. Contemporary injection molding machines require fine tuning to ensure successful mold filling and the attainment of satisfactory product quality. Addressing plastic product design from a manufacturing perspective helps ease challenges related to injection molding process and mold design. Process optimization commonly involves variables such as injection pressure, injection time, packing pressure, packing pressure holding time, melt temperature, mold cavity temperature and features of the cooling system. To ensure consistent product quality, it's crucial to optimize process parameters for specific cases due to substantial variability among injection molding input parameters. The use of Moldex3D software aids in identifying locations of critical values of defects, such as shrinkage and warpage. Warpage and shrinkage defects are minimized by Design of Experiments (DOE) optimization method. The results show that simulation, combined with optimization techniques, significantly improves product quality and process efficiency. Reducing warpage is achievable by extending the packing pressure time and using a lower coolant temperature. However, while extending the packing pressure time and reducing the melt temperature have a marginal effect on reducing shrinkage, higher cooling temperatures, as predicted, results in diminished shrinkage. [ABSTRACT FROM AUTHOR]

This paper investigates the Nelder-Mead, Powell, and Differential Evolution algorithms in the optimization of a multidimensional optimization problem. Actually, this type of the problem is difficult to solve since the objective function mixes quadratic terms and trigonometric functions. Optimization techniques were use; the ideal values for the design variables that minimize the objective function i discovered. Using 3D surface plots, contour plots, convergence rate plots, and fitness landscape plots, the optimization process made visible. The outcomes show how each algorithm works and behaves in terms of convergence, giving information about both how well it performs and how the objective function is distributed. The results add to our understanding of optimization strategies and offer suggestions for choosing the best algorithms for situations with similar complexity in optimization. Finally, the numerical optimization approach has been implemented by Python language. [ABSTRACT FROM AUTHOR]

In this work, population employment processes are studied. The systems of regulation of employment processes have been researched and a future issue has been set. An analysis of available software products is presented. The method of optimization using genetic algorithms was selected and based on the research of methods used in employment processes. An improved genetic algorithm modification for a practical problem is. Test results are obtained for the proposed genetic algorithm. The stages of development of the software complex of coordination of employment processes are presented. The working process of the developed software product is mentioned in detail. The software product was tested in Samarkand city and Kattakorgan city of Samarkand province. [ABSTRACT FROM AUTHOR]

One of the most crucial requirements for any power system is optimal power flow (OPF). The relevant optimisation problem thus attempts to create new distributions of active and reactive power while respecting the necessarytechnological and financial limitations. Considering this as an optimisation problem that entails minimising a target function while considering practical and budgetary constraints, an attempt is made in this work to redistribute both active and theoretical power, an approach that necessitates modification and the use of several control variables in the system. The purpose of this research is thus to discuss the OPF problem based on four target functions: real power losses, fuel value, voltage enhancement, and imaginary power losses, recruiting the assistance of a particle swarm optimisation)PSO(technique. After resolving the OPF issues, the PSO outputs were thus compared to those for various other techniques, including genetic algorithms and linear programming. The proposed PSO was demonstrated to achieve lower costs and losses and an improved voltage profile in case studies involving IEEE-30 buses when implemented in MATLAB environment R2018a. PSO also reduces the target function voltage profile or voltage deviation from the initial state of 0.48378 p.u to the optimal state of 0.199181 p.u. while reducing the target function's fuel cost from a starting condition of 901.9 $/hr to an ideal state of 800.636 $/hr. [ABSTRACT FROM AUTHOR]

Linear programming is one of the basic concepts to further study in applied mathematics and optimization. If the constraints of the linear programming problem form a convex region, then the problem must have an optimal solution. Cube is convex and, in terms of geometry, cube is very special. Cube has some special properties that all edges are congruent and also the all sides are congruent. Other special properties of the cube are related to orthogonality and parallelism. This paper discus linear programming problems with cube constraints. This research is study literature research to describe linear programming problems with cube constraints on geometrical angle. Considering the peculiarities of a cube, this linear programming problem must have an optimal solution. The results show the following points: 1) A cube is a convex polyhedron; 2) The steps for solving linear programming with cube constraints are analogous to the steps for solving linear programming in two dimensions using the graphical method, finding all the vertices of the cube and calculating the value of the objective function at all of the vertices, and then determining the vertex point that produces the optimal value; 3) The problem can have a unique solution (vertex) or have infinitely many solutions (the points along the edges or on the side planes of the cube). [ABSTRACT FROM AUTHOR]

Power systems facing dynamic and nonlinear characteristics require effective optimization techniques to ensure stability, efficiency, and controllability. This paper proposes a method for optimizing power systems using a genetic algorithm that takes into account complex relationships in the power infrastructure. The genetic algorithm provides a flexible and versatile means for global optimization, providing robustness to local minima and adaptation to system dynamics. The method was applied to optimize the energy balance, minimize costs and take into account the nonlinear characteristics of the power system. Experimental results validated the effectiveness of the proposed method, demonstrating energy balance and high accuracy while accounting for complex system characteristics. However, further research is required to improve the method, including a more in-depth analysis of the influence of algorithm parameters and consideration of additional uncertainty factors. This research provides valuable insights into the field of power system optimization and provides a basis for the development of more effective control techniques in complex and dynamic energy environments. [ABSTRACT FROM AUTHOR]

Encryption is a very important technique used to protect energy transmission channels from unauthorized receiver. It can be utilized to transmit data securely over the wireless medium. Encryption techniques of wireless power transfer (WPT) are important in the research on the effects of security key for security of energy transfer to the authorized receiver. The energy encryption scheme of WPT is proposed to chaos theory. Chaos theory is applicable to the logistic map to propose as a security key to chaotically regulate the switching frequency. Furthermore, for chaos theory characteristic effect power and distance performance. Therefore, this paper investigates mainly effective power based on mobile charging application. This research is focusing on performance of energy encryption in medium field for wireless power transfer system by optimization switching frequency. This research is dedicated to the performance of energy encryption for medium field wireless power transfer system based on mobile charging application. The optimization to transport the power in this research based on power is 10W. The research utilized MATLAB simulation to compare the performance. [ABSTRACT FROM AUTHOR]

In the context of the trend of continuous digitalization of the modern world, which is certainly due to the undeniable effective work with virtual productions that allow you to train personnel, or to predict the flow of the technological process. To achieve these goals, taking into account the development of process automation tools, modern computer technologies are being introduced using predictive neural networks. In addition, there is a constant combination of new digital technologies, such as the Internet of Things, not only in everyday life, but also in industry. This is all a consequence of the transition of the old QMS to a newer CMC, which requires more modern approaches for conducting technological processes and monitoring the quality of the products received throughout the production cycle. One of such approaches is the creation of digital twins (DT) of products and production. In addition, the processes occurring in a number of hydrocarbon production devices (electrolyzers, crystallizers, distillation columns, separators) are quite complex and insufficiently studied. This creates certain difficulties in the calculation of processes, their mathematical modeling and optimization. The tasks are complicated by the non-stationarity, nonlinearity, and multidimensionality of the processes occurring in the objects, with their low informativeness. Based on all of the above, it is easy to note that such productions are dangerous. [ABSTRACT FROM AUTHOR]

This research examines the effects of fuzzy control methods and proportional-integral-derivative (PID) on the performance of Permanent Magnet Direct Current (PMDC) motors. It is possible to get a general idea of each controller's efficiency by comparing the two of them. According to the study of the literature, many researchers used traditional (PID) controllers to regulate the speed of (PMDC) motors. It is challenging to manage the parameter values in a conventional PID controller and obtain excellent, suitable characteristics. However, there are some drawbacks to the PID controller, including a high beginning overshoot, susceptibility to controller gains, and a slow reaction to unexpected disturbances. To surmount the drawbacks of the traditional PID controller, a reasonably designed PID controller with a computational optimization approach technique is suggested. Additionally, fuzzy logic-based controllers, specifically fuzzy control, are suggested in this research, and their effectiveness is compared with that of PID controllers. Results from all operators' simulations are shown and examined. When controlling the speed of (PMDC) motors, it is found that fuzzy controllers perform better than other controllers. Using MATLAB modeling, we concluded that the suggested controller would result in a faster settling time, as well as a reduced overshoot and steady-state error. [ABSTRACT FROM AUTHOR]

In this study, the response surface methodology (RSM) optimization technique was used to look at the effects of load, Pumpkin Methyl Ester (PME), and compression ratio enhanced diesel on engine performance and exhaust emission. Biodiesel blend PME (20, 40, and 60%) and compression ratio (16.5, 17.5, and 18.5) were chosen to get the maximum BTE and the least BSFC, NOx, CO, smoke, and HC. The engine was run using the RSM method, which was based on the load (0–100%). The findings showed that the load, the PME, and the compression ratio concentration of the engine had a big effect on the response variables. The analysis of variance (ANOVA) for the built quadratic models showed that each model was a good fit. Also, an optimal was found by making sure that the user-defined historical plan of an experiment was as good as it could be. The perfect study factors were a load of 50%, a PME Blend of 40%, and a compression ratio of 17.5%. This gave a maximum BTE of 32.5%. When the load is 100%, the PME Blend is 40, and the compression ratio is 16.5, the fuel use goes down by 0.2 kg/kW.h. CO and HC emissions went up by 28.34% and 20.66%, while smoke and NOx emissions went down by 14.09% and 42.04%. [ABSTRACT FROM AUTHOR]

The robotic manipulator system has demonstrated its efficacy in a variety of fields. As a result, it is essential to design an efficient controller for managing the end position of a robotic arm. Due to the rising nonlinearity and complexity of robotic manipulator systems, a typical proportional-integral-derivative controller has proven ineffective. In recent years, intelligent techniques such as fuzzy logic, neural networks, and optimization algorithms have emerged as a potent tool for managing extremely complex nonlinear processes with uncertain dynamics. In this study, a PID Controller and a Fuzzy Proportional-Integral-Derivative controller with parameters generated by a Genetic Algorithm using Absolute Error Integral as the goal function are created. In this paper, simulated output results of plants controlled by a Fuzzy ProportionalIntegralDerivative controller are presented, and the superiority of the implemented controller is demonstrated through a comparison with a conventional Proportional-Integral-Derivative controller using the Genetic Algorithm optimization technique. [ABSTRACT FROM AUTHOR]

The performance of PV systems in fractional or complicated fractional shading situations is impacted by erratic irradiance, temperature, and unpredictable weather patterns. In these conditions, maximizing the O\P of PV schemes becomes challenging. This work investigates the tracking mechanism in HOM under fractional shade environments and complex partial shade conditions to obtain maximum power (MPP) in PV system, and capture global peak (GP) instead of local peak (LP) to make the system Photoelectric stable and high efficiency. Also studying the results obtained from the newly proposed (HOA) and comparing them with smart and traditional swarm optimization techniques such as p&o, improved cuckoo search (ACS), particle swarm optimization (PSO) and dragonfly algorithm (DA). For gradient optimization with regard to PV panels, MPPT use mathematical models Comparison shows HOA outperforms other technologies and achieves the best results in terms of fast tracking and effectiveness, for stability under variable weather factors and the ability to get maximum power. This paper compares the effectiveness of a number of MPPT performances over a broad variety of shading speeds using MATLAB, and proposes a PV array configuration for various systems under various partial shading advances. [ABSTRACT FROM AUTHOR]

Buildings and the construction industry contribute up to 30% of yearly greenhouse gas (GHG) emissions worldwide. Construction carbon emissions are generally categorised into operating carbon and embodied carbon, each making varying contributions to the life cycle carbon depending on the building's characteristics. This study investigates the optimum design of three-dimensional steel framed structures. A comprehensive, detailed objective function was formulated for the lifecycle embodied energy. This function considers different life cycle phases: material extraction, material processing and component fabrication, transportation, and erection. A single-objective optimisation was performed by employing two different optimisation techniques known as Harmony Search Algorithm (HSA) and Particle Swarm Optimisation (PSO) methods. These are embedded into a bespoke code written in MATLAB. The imposed behavioural constraints, in the optimum design process, were set according to the provisions of Eurocode 3 (EC3). Three design problems were investigated to demonstrate the efficiency and robustness of the proposed methods. [ABSTRACT FROM AUTHOR]

The drinking water distribution system is a series of processes that play a role in distributing drinking water through a pipeline network starting from the processing area to the service area. Each component in this system, such as pumps, pipes, tanks, and nodes can be formulated into an optimization problem. The main objective is the minimization of the distribution costs, which depends on the energy costs, pipeline costs, tank costs, and pump costs. In the present paper, a mixed integer linear programming (MILP) model is proposed for the synthesis of water distribution system by considering the costs of the 4 components that make up the drinking water distribution system. There are five decision variables considered in this model, namely the amount of flow velocity, water flowrate, the diameter of each pipe, the diameter of the tank, and the number of pumps that will be installed in the distribution system. To find out the applicable examples of using the model, problem solving was carried out based on a quasi-case study. Then the results are obtained in the form of the optimal distribution cost value which has taken into account the amount of energy costs, pipe costs, tank costs, and pump costs in the drinking water distribution system. [ABSTRACT FROM AUTHOR]

With the expansion of the research on smart cities, unmanned aerial vehicles have gained popularity in recent years, especially in search and surveillance operations. Flying robots are efficient in covering vast areas quickly, making them ideal for tasks such as search and rescue operations, and surveillance missions. To increase the productivity of these operations multi-agent systems of drones working in cooperation should be used. However, the effectiveness of cooperative search and coverage in environments with scattered uncertainties can be limited due to operational constraints. One of the primary challenges in cooperative systems is reducing operating costs while improving or maintaining efficiency. Operational limitations such as time, number of agents, and dimensions of the surveillance region significantly impact the efficiency of a multi-agent system in cooperative search and coverage missions. By considering these operational limitations and optimizing performance accordingly, cooperative search and coverage missions can be executed efficiently with reduced operating costs. To address this challenge, in this paper, an optimization-based approach has been developed for distributed cooperative search and coverage in uncertain environments with unknown stationary targets utilizing a team of agents. This approach formulates the cooperative search and coverage mission as a multi-object optimization problem. The objective function includes minimizing the number of agents used while maximizing the coverage rate. The optimization procedure is used to compute optimal operational parameters for the mission, including the number of agents, their starting positions, and so on. The effectiveness of the proposed strategy is validated and demonstrated via cooperative search and coverage problems simulation. The mission performance constrains were fully satisfied by reaching uncertainty reduction of 0.52 (should be less than 0.6) and founded targets ratio of 0.75 (should be greater than 0.7). The simulation results show that the proposed approach can significantly improve mission efficiency while reducing operating costs. [ABSTRACT FROM AUTHOR]