particle swarm optimization algorithm

In this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controller (LOC) and the particle swarm algorithm (PSO). The control bank in the multi-model controller includes three LOC controllers that generate optimal signals through the linearization of the nonlinear equations of the system and the minimization of an energy function to be combined by the Bayes recursive algorithm simultaneously to the generator excitation system and SVC. In order to generate an optimal linear signal, Riccati's equation must be solved; Riccati's equation includes two weight matrices Rric and Qric. These matrices elements are optimized by PSO algorithm. The PSO algorithm has calculated the optimal Rric and Qric with two different objective functions of maximizing the eigenvalues and minimizing the area under the speed curve. To evaluate the MMC-LOC-PSO control strategy, the symmetrical three-phase error is applied to the worst bus and the results of these two objective functions are compared. The simulation of the single machine power system has been done by MATLAB. The proposed control strategy, while maintaining stability, also effectively damps the LFOs, in addition, the permanent rotor speed and rotor angle error have also been favorably pushed to zero.

So far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is essential to study motor performance and predict its behavior. Therefore, an efficient, accurate and reliable parameter estimation method is needed. In this article, the problem of estimating the parameters of the transfer function of the inverter-fed BLDC motor set has been solved using particle swarm algorithms (PSO). The results of using this algorithm have been compared with the results of other optimization algorithms. The comparison of these results has shown that the PSO algorithm is an efficient, accurate and reliable method for solving the transfer function parameter estimation problem.

Special characteristics of the DC motors such as high reliability, flexibility, low consumption and simplicity of control have expanded the use of these motors in different industrials for instance steel plants, electric trains and etc. However, in the majority of applications, this system still controlled via traditional Proportional Integral Derivative (PID) controllers and in some cases, these controllers have been adjusted with intelligent methods. But this paper, in order to DC motor speed control, suggests a novel method to create a fuzzy logic inference system that is completely optimized through Particle Swarm Optimization (PSO) algorithm. The proposed approach has applied to a DC motor model in the MATLAB/Simulink software simulation environment and compared with different methods based on PID controllers. Simulation results show the suggested approach improved the various time response terms such as rise time, delay time and settling time for DC motor speed control.

This research addresses a simultaneous jobs scheduling and worker assignment problem in flow shop environment in which there are some workers with different skills who can operate the jobs with different speed. The primary aim of the research is to schedule the jobs and assign the worker so that maximum completion time (Cmax) is minimized. To tackle this problem, a mixed integer linear programming model is introduced and is coded in CPLEX software so that it can obtain the optimal solutions in reasonable time. Due to NP-hardness of the research problem, CPLEX cannot achieve the optimal solutions for large-scale problems. Thus, two metaheuristic approaches based on particle swarm optimization (PSO) is proposed here. In order to trapping the PSO algorithm in local optima with high probability, the performance of the PSO algorithm is improved by simulated annealing (SA) algorithm (IPSO). The experimental results show that the IPSO algorithm can generate better results in entire scales and the superiority of the IPSO is significant in the large scale.

The use of distributed generation resources is an effective way for responding to load growth and providing a certain level of reliability. Solar power plants are of widely used distributed generation systems. Due to the particular importance of logging out a system of operating mode, which can interrupt a large number of customers served, different reliability indices are considered. Installing solar power plants in a distribution network affects voltage at different points. Therefore, placement and amount of power injected by solar cells into busbars should be determined so that to have maximum impact on the distribution system in terms of improving voltage quality and enhancing reliability. The present study aimed to find a suitable place for a solar plant, resulting in reduced system losses and enhanced reliability indices, using multi-objective particle swarm optimization algorithm. The proposed method is implemented using the MATLAB software on the Billington system (RBTS).The results show that solar power plants can have positive effects, such as improving reliability and improving the voltage quality on distribution networks, depending on the specifications, technology and location of the network connection