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A multi-objective Hybrid Algorithm for Optimal Planning of Distributed Generation

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第4期45卷 3035-3054页

作者： Pandey, Ravi Shankar Awasthi, S. R. Rabindranath Tagore Univ Dept Elect Engn Bhopal Chiklod Rd Bhopal 464993 Madhya Pradesh India

Distributed generations (DGs) have been constantly incorporating into the distribution systems. The optimal planning (sizing and sitting) of the DGs by applying harmony search (HS)-based hybrid genetic algorithm integrated adaptive particle swarm optimization (GA-APSO) technique is exhibited in this work. The fitness evolution function has investigated as the multi-objective function (FMO), which depends on the five significant indexes such as active power loss, reactive power loss, total cost generation index, voltage deviation, and load balancing index. The ideal solution has attained by minimizing the multi-objective (MO) fitness function by deploying HS-based GA-PSO strategy. The projected work will be implemented on IEEE standard 33- and 69-bus radial distribution networks. The performance of the suggested algorithm has analyzed dependent on the five measures, such as active and reactive power loss, voltage profile improvement, reduction in voltage deviation, and cost of operation of generation. The legitimacy of the assessed outcomes has affirmed by comparing with some of the notable optimization algorithms.

关键词： Distributed generation (DG) multi-objective function Optimal planning Optimal power flow Harmony search optimization Hybrid GA-PSO Active and reactive power loss voltage deviation

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An optimal load distribution method for distributed energy systems based on the improved particle swarm optimisation

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2024年第6期46卷 651页

作者： Liu, Junjun Changshu Inst Technol Changshu Jiangsu Peoples R China Jiangsu Key Lab Elevator Intelligent Safety Changshu Jiangsu Peoples R China

In order to solve the problem of large load variance and high distribution scheme cost after the distributed energy system is integrated into the traditional large power grid, an optimal load distribution method for distributed energy systems based on the improved particle swarm optimisation algorithm is proposed in this paper. Firstly, four output models of the distributed energy system are established. With the minimum cost and the minimum system load variance as the objectives, a multi-objective function model is constructed. Considering the power limit, generation power limit and other restrictions, constraints are constructed to complete the construction of the optimal load distribution model of the distributed energy system. Finally, the PSO algorithm is introduced to update the optimal particles in the solution space through different iterative processes. Combined with quantum theory, the PSO algorithm is optimised to obtain the optimal load distribution scheme. The results show that the cost of the distribution scheme obtained by this method can be reduced by more than 30,000 Yuan, and its load variance value is smaller, so the method has certain research value.

关键词： improved particle swarm optimisation distributed energy system output model multi-objective function constraints load distribution

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An Enhanced multi-objective-Derived Adaptive DeepLabv3 Using G-RDA for Semantic Segmentation of Aerial Images

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第8期48卷 10745-10769页

作者： Anilkumar, P. Venugopal, P. Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India

Semantic segmentation acts as a major role in classifying the remote sensing images into oceanic ice, vegetation, roads, vehicles, houses, and more for offering high precision at the pixel level. In recent studies, convolutional neural network (CNN) has accomplished superior efficiency in the semantic segmentation of images. Even though various deep techniques and architectures have been utilized for enhancing the accuracy, it suffers from classifying the confused classes. Due to the optical conditions and remote sensing information, the sub-decimeter aerial imagery segmentation is challenging while achieving fine-grained semantic segmentation outcomes. The core goal of this task is to adopt the latest Adaptive DeepLabv3 strategy for enhanced semantic segmentation of aerial images. In Adaptive Deeplabv3, the involvement of both the encoder-decoder structure and spatial pyramid pooling module with adaptiveness by a hybrid meta-heuristic algorithm makes faster and stronger segmentation performance within less search space and reduced computation time. The relevant parameters of DeepLabv3 are tuned or optimized by the hybrid meta-heuristic algorithm based on Genetic Inspired Red Deer Algorithm (G-RDA). The enhanced segmentation is employed concerning a fitness function with precision and accuracy. Finally, the experimental analysis of the suggested Adaptive DeepLabv3 strategy for semantic segmentation of aerial images proves its competitive solution when evaluated over conventional approaches.

关键词： Semantic segmentation Aerial images Adaptive DeepLabv3 Genetic inspired red deer algorithm multi-objective function Adaptive deep learning

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INFLUENCE OF objective functionS IN STRUCTURAL DAMAGE IDENTIFICATION USING REFINED AND SIMPLE MODELS

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2009年第4期9卷 607-625页

作者： Perera, Ricardo Fang, Sheng-En Tech Univ Madrid Dept Struct Mech Madrid 28006 Spain

The most usual approach for solving damage identification problems is the use of the finite element (FE) model updating method. To apply the method, a minimization of an objective function measuring the fit between measured and model predicted data is performed. Then, the success of the procedure depends strongly on the accuracy of the FE model and the choice of a suitable objective function. Although detailed FE models provide an accurate means for calculating the dynamic response of the structure, their size and complexity involve a large number of parameters to be updated and a high computational cost. In order to shorten the computational time, more simplified and practical models able to model the global dynamic response of the structure accurately would be desirable. Furthermore, working with several objective functions instead of only one would increase the robustness and performance of the procedure. In this paper, a multi-objective simple beam model is proposed and compared with a more re fined model based on plane elements. Furthermore, in the multi-objective framework, different combinations of objective functions are studied. The reliability and effectiveness of the proposed model has been evaluated in a damage detection problem of a reinforced concrete frame experimentally tested under different levels of damage.

关键词： Damage identification FE model updating reinforced concrete frame damage function multi-objective function

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Solving multi-objective optimal power flow problem considering wind-STATCOM using differential evolution

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Frontiers in Energy 2013年第1期7卷 75-89页

作者： Belkacem MAHDAD K. SRAIRI LMSE Laboratory Department of Electrical Engineering University ofBiskra Biskra 07000 Algeria

In this paper, a simple strategy based differential evolution was proposed for solving the problem of multi-objective environmental optimal power flow considering a hybrid model （Wind-Shunt-FACTS）. The DE algorithm optimized simultaneously a combined vector control based active power of wind sources and reactive power of multi STATCOM exchanged with the electrical power system to minimize fuel cost and emissions. The proposed strategy was examined and applied to the standard IEEE 30-bus with smooth cost function to solve the problem of security environmental economic dispatch considering multi distributed hybrid model based wind and STATCOM controllers. In addition, the proposed approach was validated on a large practical electrical power system 40 generating units considering valve point effect. Simulation results demonstrate that choosing the installation of multi type of FACTS devices in coordination with many distributed wind sources is a vital research area.

关键词： differential evolution multi-objective function optimal power flow economic dispatch valve point effect environment wind source STATCOM

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A PSO-based multi-objective Method for Optimal Weight Factors, Placement and Sizing of multiple DG Units in a Distribution System 21

A PSO-based Multi-objective Method for Optimal Weight Factor...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abdel-Salam, Mazen El-Mohandes, Mohamed Th Mahmoud, Lubna Assiut Univ Elect Engn Dept Assiut Egypt Nanda Univ Elect & Renewable Energy Engn Dept Bani Suwayf Egypt

ISBN: (纸本)9781728152899

Minimization of a single-objective-function (SOF) and a weighted-sum multi-objective-function (MOF) is aimed at obtaining optimal placements and sizes of multi DG units in distribution systems. The indices of the MOF are related to characteristics of the distribution system. The weight factors were assumed arbitrarily or determined according to the preferences of the decision maker. To the authors' knowledge, this paper presents -for the first time- a Particle-Swarm-Optimization (PSO) -based-method for computing the weighting factors while minimizing the SOF or MOF of the distribution system. The index priority of the MOF is always given to the deviation of bus-voltage from nominal-value in conformity to power-quality features regarding no sag or swell of bus-voltage. The computed optimal placements and sizes of the DG units agreed reasonably with those reported in the literature. The dependency of optimal placements and sizes on the load type is investigated for the IEEE 33-bus radial distribution system.

关键词： multi-objective function distributed generation load types particle swarm optimization active and reactive power losses

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Design Optimisation of Permanent Magnet Synchronous Motor for Electric Vehicles Traction using the Bees Algorithm 49

Design Optimisation of Permanent Magnet Synchronous Motor fo...

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49th International Universities Power Engineering Conference (UPEC)

作者： Braiwish, N. Y. Anayi, F. J. Fahmy, A. A. Eldukhri, E. E. Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF10 3AX S Glam Wales

ISBN: (纸本)9781479965571

Electric machines designs for traction application are concerned with paying particular attention to power density and efficiency. Therefore, this paper is focused on applying Bees algorithm (BA) for optimal design of Brushless Permanent Magnet Synchronous Motor (BLPMSM) for propulsion application. The analytical approach for the motor magnetic circuit is performed using the radial instantaneous magnetic field distribution in the airgap under specified loading condition;taking into account the magnetic core saturation and motor overall performance. The design aims to maximize the power density. Therefore, the optimisation objective function is formed to minimise motor weight and maximise efficiency. While the Bees Algorithm (BA) is applied to search for the optimum design parameters;the optimised design is then verified using Finite Element Method (FEM). Comparing with an existing machine, called here basic motor;the obtained results show that motor weight can be reduced by approximately 20%, while motor output power is kept constant. As the motor is designed for traction applications, the characteristics of the developed torque and speed were investigated under different gearing levels using magnetic gearing technique, the efficiency shows improvements in comparison to the basic motor.

关键词： Bees Algorithm Brushless Permanent Magnet Synchronous Motor BLPMSM Finite Element Method FEM Magnetic Gearing multi-objective function

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User Friendly Generator Maintenance Scheduling Simulation System based on Probabilistic Methodology

User Friendly Generator Maintenance Scheduling Simulation Sy...

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International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Lee, Yeonchan Choi, Jaeseok Jung, Myeunghoon Gyeongsang Natl Univ EE Jinju GN South Korea Korea South East Power Corp KOEN Jinju GN South Korea

ISBN: (纸本)9781509019700

This study develops alternative user friendly generator maintenance scheduling(GMS) considering not only probabilistic reliability maximization but also probabilistic production cost minimization. Furthermore, the proposed GMS system has various kinds of objective functions as like as CO2 minimization. The probabilistic reliability objective includes LOLE(Loss of load expectation), EENS(Expected energy not served) and EIR(Energy index of reliability). Production cost is evaluated with consideration of uncertainties of generators. In actual system case study describes effectiveness and user friendly of generator maintenance scheduling simulation systems proposed in this paper. Additionally, the practicality and effectiveness of the proposed approach are demonstrated in the simulation for a real-size power system model in Korea South-East Power CO.(KOEN).

关键词： Optimal generator maintenance scheduling Probabilistic reliability evaluation Probabilistic production cost simulation multi-objective function

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multi-objective Optimization of the Number of Transport Resources in the High-Bay Warehouse 3

Multi-Objective Optimization of the Number of Transport Reso...

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3rd IEEE International Conference on Control, Automation and Robotics (ICCAR)

作者： Alszer, Sara Krystek, Jolanta Silesian Tech Univ Fac Automat Control Elect & Comp Sci Gliwice Poland

ISBN: (纸本)9781509060887

The transport plays a growing role in the integrated logistics management of semi-finished products and components flow. For manufacturing companies it is important to maximize the use of production resources, minimize the number of transport resources and optimize the process, in terms of both production costs and time. The use of specific means of transport and correct organization of material flow are fundamental in order to assure continuity of production and to deliver products to the customer just in time. Due to highly complicated processes of organization and management of material flow, computer systems are currently used. Based on this type of application, some necessary simulations were carried out. These tests were aimed at investigating the impact of the number of transport resources on handling time of high-bay warehouse. On this basis, a methodology of defining a multi-objective function and determining weights of each objective are presented. The conducted analysis allowed to select the most effective number of internal transport resources in exemplary warehouse. The obtained results, particular stages of conducted analysis and conclusions were presented in this paper.

关键词： transport high-bay warehouse multi-objective function optimization

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Optimal Planning of Distributed Generation Using Improved Grey Wolf Optimizer and Combined Power loss Sensitivity

Optimal Planning of Distributed Generation Using Improved Gr...

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IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Sodani, Mohamed Aly, Hamed H. Little, Timothy A. Dalhousie Univ Elect & Comp Engn Halifax NS Canada

ISBN: (纸本)9781665448642

This paper introduces a hybrid method for finding the best location and size of distribution generation (DG) sources in a distribution system. The strategy employs Combined Power Loss Sensitivity (CPLS) and the algorithm Improved Grey Wolf Optimizer (I-GWO), with CPLS determining candidate locations for DG, and I-GWO determining the best location and size based on CPLS suggestions for candidate buses. The overall aim of this approach is to improve system stability, enhance voltage profile, and minimize power loss. The work evaluates the novel strategy using IEEE-33 and IEEE-69 bus radial distribution systems and investigates three kinds of DG to make comparisons of key efficiency and performance metrics. The test results show that, in comparison to Other optimization methods, the proposed hybrid approach with multi-objective functions offers optimal results.

关键词： Distributed Generation Optimization Improved Grey Wolf Optimizer multi-objective function

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