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Particle swarm optimization algorithm for simultaneous optimal placement and sizing of shunt active power conditioner(APC)and shunt capacitor in harmonic distorted distribution system

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Journal of Central South University 2017年第9期24卷 2035-2048页

作者： Mohammadi Mohammad Department of Electrical Engineering Borujerd BranchIslamic Azad UniversityBorujerdIran

Due to development of distribution systems and increase in electricity demand,the use of capacitor banks *** the other point of view,nonlinear loads generate and inject considerable harmonic currents into power *** this condition if capacitor banks are not properly selected and placed in the power system,they could amplify and propagate these harmonics and deteriorate power quality to unacceptable *** attention of disadvantages of passive filters,such as occurring resonance,nowadays the usage of this type of harmonic compensator is *** the other side,one of parallel multi-function compensating devices which are recently used in distribution system to mitigate voltage sag and harmonic distortion,performs power factor correction,and improves the overall power quality as active power conditioner(APC).Therefore,the utilization of APC in harmonic distorted system can affect and change the optimal location and size of shunt capacitor bank under harmonic distortion *** paper presents an optimization algorithm for improvement of power quality using simultaneous optimal placement and sizing of APC and shunt capacitor banks in radial distribution networks in the presence of voltage and current *** algorithm is based on particle swarm optimization(PSO).The objective function includes the cost of power losses,energy losses and those of the capacitor banks and APCs.

关键词： shunt capacitor banks active power conditioner multi-objective function particle swarm optimization (PSO) harmonic distorted distribution system

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Color Formulation of Cotton Fabrics Using multi-objective Ant Colony Optimization

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JOURNAL OF NATURAL FIBERS 2022年第17期19卷 15459-15474页

作者： Chaouch, Sabrine Moussa, Ali Ladhari, Neji Univ Monastir Text Engn Lab LGTex Monastir Tunisia Univ Monastir Higher Inst Fash Monastir ISMM Monastir Tunisia Univ Monastir Natl Engn Sch Monastir ENIM Monastir Tunisia

The formulation of colors is one of the most important aspects of colorimetry. For dyeing and finishing industries, mastering this step is a major problem to be solved. In this paper, a multi-objective ant colony optimization algorithm is developed to optimize the reproduction of the desired shades. The multi-objective function of the proposed algorithm minimizes simultaneously the CMC color differences and the metamerism index between the standard and the reproduced colors. Two ranges of dyes were used to dye cotton fabrics. The elaborated multi-objective algorithm showed good results with small values of color differences and metamerism index. All CMC color differences values were less or at the limit of the acceptability threshold of 1, and about 75% of samples exhibited minimum metamerism and are considered as a good match.

关键词： Color formulation ant colony algorithm multi-objective function CMC color differences metamerism index

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Research on topology optimization of liquid-cooled plates based on multi-level optimization

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 155卷

作者： Xie, Liyao He, Zhaowei Zhao, Yulong Vulin, Domagoj Bhayo, Barkat Ali Hebei Univ Technol Sch Energy & Environm Engn Tianjin 300401 Peoples R China Univ Zagreb Fac Min Geol & Petr Engn Pierottijeva 6 Zagreb 10000 Croatia Lappeenranta Lahti Univ Technol LUT Sch Energy Syst Lappeenranta Finland

This paper employs topology optimization techniques to enhance the performance of liquid-cooled plates while investigating the optimization process and its outcomes. A multilevel optimization strategy is implemented to refine the accuracy and smoothness of the optimization process. The study demonstrates that expanding the range of penalty factors and refining the granularity of penalty factors can effectively enhance the capability of multilevel optimization in suppressing the formation of intermediate densities. Furthermore, a multi-objective function is utilized to strike a balance between the heat transfer efficiency and the hydraulic performance of the liquid-cooled plate. Ultimately, after considering aspects related to heat transfer and hydraulic performance, the research finds that compared to traditional straight-channel liquid-cooled plates, liquid-cooled plates featuring streamlined multi-branched flow channels not only increase the heat transfer area but also reduce pressure drop. As a result, this design improves the temperature uniformity of the liquid-cooled plate and enhances the velocity uniformity of the cooling fluid. At different inlet velocities, the topology-optimized liquidcooled plate exhibits a reduction in both effective thermal resistance and pumping power compared to the traditional straight-channel liquid-cooled plate.

关键词： Topology optimization Liquid-cooled plate multi-level optimization multi-objective function Thermal and hydraulic performance

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The optimum computational simulation of MHD natural convection for improved cooling efficiency and entropy performance inside Μ-shaped cabinet

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NUMERICAL HEAT TRANSFER PART A-APPLICATIONS 2024年第10期85卷 1633-1652页

作者： Azzawi, Itimad D. J. Al-Damook, Amer Univ Diyala Mech Engn Dept Baqubah Iraq Univ Anbar Renewable Energy Res Ctr Ramadi 31001 Iraq

The application of natural convection heat transfer within a novel Mu-shaped cabinet filled with single-phase pure water/metal foam is investigated numerically. Natural convection is modeled throughout the porous zone using the Local-Thermal-Equilibrium mode and Darcy-ForchheimerBrinkman law. A novel Mu-shaped cabinet with varies Mu ratio (0 <= Mu <= 0.8) and temperature difference (10 K <= Delta T <= 40 K) is applied at a constant porosity of epsilon = 0.85. Furthermore, the current research is conducted to analyze the response surface methodology (RSM) accompanied by a computational simulation for optimizing the multi-objective function of the Mshaped cabinet in terms of two computed responses: maximizing the Nusselt number ratio (NNR) and minimizing the entropy generation ratio (EGR). The optimization study considers the various pore per inch (10 <= PPI <= 50), Darcy number (10(-9) <= Da <= 10-1) and uniform magnetic fields (0 <= Ha <= 100). The results showed that the optimum working conditions consistent with the desired aim are achieved in the maximization of NNR by nearly 26.84 times and the minimization of EGR of 0.895 obtained at Ha = 100, Da = 10(-1) and PPI = 30. Thus, the current investigation is a unique application study of CFD and RSM which provides a helpful reference for the optimum design cooling efficiency and entropy performance of a novel Mu-shaped cabinet.

关键词： MHD natural convection augmentation multi-objective function porous Mu-shaped cabinet response surface methodology (RSM)

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An enhanced deep learning-based disease detection model in wireless body area network with energy efficient routing protocol

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WIRELESS NETWORKS 2024年第4期30卷 2961-2986页

作者： Liya, B. S. Krishnamoorthy, R. Arun, S. Easwari Engn Coll Dept Comp Sci & Engn Chennai 600089 Tamil Nadu India Chennai Inst Technol Dept ECE Chennai 600069 Tamil Nadu India Chennai Inst Technol Ctr Adv Wireless Integrated Technol Chennai 600069 Tamil Nadu India Jerusalem Coll Engn Dept ECE Velachery Rd Chennai 600100 Tamil Nadu India

The group of connected small "Bio-sensor nodes (BSNs)" is employed in various parts of the human body that is called "Wireless body area networks (WBAN)". It helps to recognize health-related data and to monitor the readings of blood pressure, "Electro-Cardiogram (ECG)", heartbeat rate, "Electro-Myography (EMG)", and glucose levels in the blood of the human body to know the real-time health. Many applications and research areas use the WBAN, like sports, social welfare, medical field, and entertainment. For WBAN, the major backbone is the BSNs, generally known as "Sensor nodes (SNs)". Based on the small size of the SNs, they have basic resources. High energy is consumed when there is heavy data transmission. When all the energy is drained, that leads to the death of some SN. Routing is the data transfer method from the main source to the sink nodes. The minimum number of SNs is the efficient routing in the data transmission process, resulting in maximum energy consumption. Hence, an energy-efficient routing scheme is implemented with heuristic approaches to conserve more energy in the WBAN. To perform routing effectively, the Cluster Head (CH) needs to be selected initially. In this work, the optimal selection of the CH is carried out using a hybrid Red piranha and egret swarm algorithm (RPESA). Once the CH is optimally selected, the optimal routing is implemented using the RPESA algorithm. The data transmitted using this optimal routing scheme is then utilized for disease diagnosis using an Adaptive dilated cascaded recurrent neural network (ADC-RNN). The parameters in the ADC-RNN technique are optimally selected using the same RPESA algorithm. The classified disease outcome was obtained from ADC-RNN. The suggested heuristic-based energy-efficient routing approach for WBAN and the deep learning-based disease detection model was implemented, and its function was validated by differentiating it with other existing schemes.

关键词： Wireless body area network Energy efficient routing multi-objective function Red piranha and egret swarm algorithm Adaptive dilated cascaded recurrent neural network

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Optimal placement of switches in a radial distribution network for reliability improvement

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 76卷 53-68页

作者： Ray, Saheli Bhattacharya, Aniruddha Bhattacharjee, Subhadeep Natl Inst Technol Agartala Agartala 799046 India

This paper proposes differential search (DS) algorithm to solve distribution system reliability optimization problem. Optimum number and location of remote control switch (RCS) have been found, in order to enhance reliability of a radial distribution system. A multi-objective formulation has been considered with a view to enhance reliability at a compromised cost. DS algorithm utilizes the Brownian-like random-walk movement used by an organism to migrate. Simulation results obtained by DS algorithm have been compared with that of particle swarm optimization (PSO), differential evolutionary algorithm (DE), genetic algorithm (GA), ant colony optimization (ACO) and gravitational search algorithm (GSA). Results show that DS algorithm provides considerably superior performance, in terms of quality of solution obtained and computational efficiency. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Differential search algorithm multi-objective function Radial distribution system Remote control switches Repair time Restoration time

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Self-Adaptative Troubleshooting for to Guide Resolution of Malfunctions in Aircraft Manufacturing

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IEEE ACCESS 2021年 9卷 42707-42723页

作者： Ramos-Gutierrez, Belen Teresa Gomez-Lopez, Maria Borrego, Diana Ceballos, Rafael Gasca, Rafael M. Barea, Antonio Univ Seville Escuela Tecn Super Ingn Informat Dept Lenguajes & Sistemas Informat Seville 41012 Spain Airbus Def & Space San Pablo Sur Seville 41020 Spain

The increasing complexity of systems and the heterogeneous origin of the possible malfunctions bring about the necessity of redefining the troubleshooting processes. Troubleshooting comprises the set of steps for the systematic analysis of the symptoms after the detection of a malfunction. The complexity of certain systems, such as aircraft, means the origin of that malfunction can be any of several reasons, where diagnosis techniques support engineers in determining the reason for the unexpected behaviour. However, derived from the high number of components involved in an aircraft, the list of possible fault origins can be extremely long, and the analysis of every element on the list, until the element responsible is found, can be very time-consuming and error-prone. As an alternative, certain input/output signals can be read to prevent the substitution of a correctly functioning component, by validating its behaviour in an indirect way. In order to optimise the actions to perform, we have identified the relevant parts of the model to propose a troubleshooting process to ascertain the signals to read and the components to substitute, while striving to minimise the action cost in accordance with a combination of structural analysis, the probability of malfunction associated to the components, and the cost associated to each extra signal read and component substituted. The proposal has been validated in a system taken from a real scenario obtained in collaboration with the Airbus Defence and Space company. A statistical analysis of the degree of improvement of the troubleshooting process has also been included.

关键词： Switches Proposals Aircraft Observability Atmospheric modeling Manufacturing Complexity theory Decision-making process model-based diagnosis multi-objective function troubleshooting

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Deep Learning-based Prioritized Packet Classification and Optimal Route Selection in Wireless multimedia Sensor Networks

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IETE JOURNAL OF RESEARCH 2024年第2期70卷 1519-1537页

作者： Chiwariro, Ronald Thangadurai, N. JAIN Deemed be Univ Fac Engn & Technol Dept Comp Sci & Engn Bangalore India Sankalchand Patel Univ Ctr Res & Innovat Dept Informat & Commun Technol Visnagar Gujarat India

Wireless multimedia sensor networks suffer network congestion due to increased traffic leading to data packet loss and increased energy consumption. Thus, it is essential to have a mechanism for priority-based packet classification to improve the performance and decrease the delay in real-time packets. This paper introduces priority-based packet classification using a word embedding mechanism to extract packet semantics and classification using the long short-term memory model that works based on the different characteristics of the packet header to decide the priority of each packet. Furthermore presents a hybrid meta-heuristic algorithm termed butterfly-based rider optimization algorithm, which considers the packet priority and other parameters in optimal route selection. This hybrid meta-heuristic algorithm is developed by merging rider optimization algorithm, and butterfly optimization algorithm. Finally, the experimental results validate the analytical result as well as the performance comparison between the proposed optimal route selection model and the conventional models.

关键词： Butterfly-based rider optimization algorithm Deep learning Hybrid meta-heuristic algorithm LSTM multi-objective function Network congestion Optimal route selection Prioritized packet classification Quality of Service RNN Wireless multimedia sensor networks

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multi-objective reward shaping for global and local trajectory planning of wing-in-ground crafts based on deep reinforcement learning

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AERONAUTICAL JOURNAL 2024年第1320期128卷 371-397页

作者： Hu, H. Li, D. Zhang, G. Zhang, Z. Dalian Univ Technol Sch Naval Architecture Engn State Key Lab Struct Anal Ind Equipment Dalian Peoples R China Beihang Univ Sch Aeronaut Sci & Engn Beijing Peoples R China Collaborat Innovat Ctr Adv Ship & Deep Sea Explora Shanghai Peoples R China

The control of a wing-in-ground craft (WIG) usually allows for many needs, like cruising, speed, survival and stealth. Various degrees of emphasis on these requirements result in different trajectories, but there has not been a way of integrating and quantifying them yet. Moreover, most previous studies on other vehicles' multi-objective trajectory is planned globally, lacking for local planning. For the multi-objective trajectory planning of WIGs, this paper proposes a multi-objective function in a polynomial form, in which each item represents an independent requirement and is adjusted by a linear or exponential weight. It uses the magnitude of weights to demonstrate how much attention is paid relatively to the corresponding demand. Trajectories of a virtual WIG model above the wave trough terrain are planned using reward shaping based on the introduced multi-objective function and deep reinforcement learning (DRL). Two conditions are considered globally and locally: a single scheme of weights is assigned to the whole environment, and two different schemes of weights are assigned to the two parts of the environment. Effectiveness of the multi-object reward function is analysed from the local and global perspectives. The reward function provides WIGs with a universal framework for adjusting the magnitude of weights, to meet different degrees of requirements on cruising, speed, stealth and survival, and helps WIGs guide an expected trajectory in engineering.

关键词： Wing-in-ground craft Trajectory planning multi-objective function Deep reinforcement learning Reward shaping

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A new estimation approach based on moments for estimating Weibull parameters in wind power applications

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ENERGY CONVERSION AND MANAGEMENT 2018年 164卷 570-578页

作者： Usta, Ilhan Arik, Ibrahim Yenilmez, Ismail Kantar, Yeliz Mert Anadolu Univ Sci Fac Dept Stat TR-26470 Eskisehir Turkey Bilecik Seyh Edebali Univ Sci & Art Fac Dept Stat TR-11230 Bilecik Turkey

This study introduces a new estimation approach that could be used in calculating the Weibull parameters for the estimation of wind power. This new approach, called the method of multi-objective moments (MUOM), proposes minimization of the squared deviances between the first three population moments and corresponding sample moments. In the proposed MUOM, the third moment, which is used in the formula of wind power, is a key function since it provides a significant contribution to the correct prediction of wind energy. In order to demonstrate the performance of the proposed MUOM, it is compared with the well-known estimation methods such as the maximum likelihood, modified maximum likelihood, moment and energy pattern factor. In comparative analysis, the performance of the considered methods is evaluated on the actual wind speed data measured different time periods according to various goodness of fit criteria. The obtained results indicate that the MUOM definitely provides more accurate estimates than other well-known methods in estimating wind power based on the Weibull distribution. Thus, the MUOM can be used as an improved method in estimating wind power.

关键词： Weibull distribution Moments multi-objective function Wind power Comparative analysis

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