scholarly journals Electric Power Material Logistics Distribution Based on Improved Multi-Population Particle Swarms

2021 ◽  
Vol 2143 (1) ◽  
pp. 012013
Author(s):  
Jie Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Power ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Reducing Power ◽  
Power Supplies ◽  
Distribution Model ◽  
Swarm Optimization ◽  
Power Cost ◽  
Logistics Distribution

Abstract In view of the problem that the information requirements of electric power supplies logistics distribution are increasingly high, this study proposes the research of electric power supplies logistics distribution based on improved multi-population particle swarm. Firstly, the simulated annealing algorithm is introduced in detail, and then the particle swarm optimization (PSO) is improved by using the domain extension method. Then the multi-population particle swarm optimization was carried out through the optimization of coding system, the updating speed formula and the algorithm convergence control. Finally, the optimization effect was verified. Final results show that, this study put forward based on improved particle swarm more population of electricity supplies logistics distribution model has feasibility and suitability, through the optimized parameters and algorithm of the model, to identify the logistics network of electric power supply company the best optimization model, thus reducing power cost of inventory and logistics distribution time, so as to improve the efficiency of logistics distribution.

scholarly journals Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3112
Author(s):  
Donghyeon Lee ◽  
Seungwan Son ◽  
Insu Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Power ◽  
Distributed Generation ◽  
Optimal Allocation ◽  
Power Grid ◽  
Particle Swarm ◽  
Renewable Energy Resources ◽  
Swarm Optimization ◽  
Large Capacity ◽  
Electric Power Grid

Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.

scholarly journals The Cold Rolling Load Distribution of the Nuclear Power Zirconium Alloy Based on the Self-adaptive Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Cao Yuan ◽  
Jianguo Cao ◽  
Wang Tao ◽  
Wang Leilei ◽  
Li Fang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Cold Rolling ◽  
Optimization Algorithm ◽  
Load Distribution ◽  
Zirconium Alloy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Rolling Process ◽  
Distribution Model ◽  
Swarm Optimization

Abstract Aiming at the problem of load distribution during multi-pass cold rolling of nuclear zirconium alloy strip, the load distribution model with good shape is established by the self-adaptive particle swarm optimization algorithm (SAPSO), considering the main constraint conditions including rolling force, reduction and torque in cold rolling process. Based on the penalty function method transforming the constraint problem into the unconstrained problem, the particle swarm optimization algorithm with adaptive inertia weight factor optimized the load distribution model is developed to improve the local search ability of the particle swarm optimization algorithm. Compared with the existing nuclear zirconium alloy industrial schedule, the simulation results of load distribution based on the SAPSO can keep good shape in multi-pass cold rolling process with the high prediction accuracy. The industrial experiments demonstrate that the proportional crown difference value is consistent, the plate shape flatness is good.

scholarly journals Perbandingan Metode Gaussian Particle Swarm Optimization (GPSO) dan Lagrange Multiplier pada Masalah Economic Dispatch

2012 ◽  
Vol 3 (1) ◽  
pp. 228
Author(s):  
Siti Komsiyah
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Power ◽  
Lagrange Multiplier ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Economic Planning ◽  
Electric Power System ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Economic Dispatch Problem

On electric power system operation, economic planning problem is one variable to take into account due to operational cost efficiency. Economic Dispatch problem of electric power generation is discussed in this study to manage the output division on several units based on the the required load demand, with minimum operating cost yet is able to satisfy equality and inequality constraint of all units and system. In this study the Economic Dispatch problem which has non linear cost function is solved using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving is inspired by swarm intelligent and probabilities theories. To analize its accuracy, the Economic Dispatch solution by GPSO method is compared with Lagrange Multiplier method. From the test result it is proved that GPSO method gives economic planning calculation better than Lagrange Multiplier does.

scholarly journals A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5679
Author(s):  
Mohamed A. M. Shaheen ◽  
Dalia Yousri ◽  
Ahmed Fathy ◽  
Hany M. Hasanien ◽  
Abdulaziz Alkuhayli ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Electric Power ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Reactive Power ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Swarm Optimization ◽  
Marine Predators

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

scholarly journals Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Wang ◽  
Yongqiang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Correlation Algorithm ◽  
Annealing Algorithm

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

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