Bacterial graphical user interface oriented by particle swarm optimization strategy for optimization of multiple type DFACTS for power quality enhancement in distribution system

This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems.The proposed GUI based toolbox,allows the user to choose between single and multiple DFACTS allocations,followed by the type and number of them to be allocated.The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS.This is found to be faster and provides more accurate results compared to the usual PSO and BFO.Results obtained with MATLAB/Simulink simulations are compared with PSO,BFO and enhanced BFO.It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality.Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC,APC and DSTATCOM is preferable to reduce power losses,improve load balancing and enhance voltage deviation index to 70%,38% and 132% respectively and also it can improve loading factor without additional power loss.

Power loss reduction of distribution systems using BFO based optimal reconfiguration along with DG and shunt capacitor placement simultaneously in fuzzy framework

In distribution systems,network reconfiguration and capacitor placement are commonly used to diminish power losses and keep voltage profiles within acceptable limits.Moreover,the problem of DG allocation and sizing is great important.In this work,a combination of a fuzzy multi-objective approach and bacterial foraging optimization(BFO) as a meta-heuristic algorithm is used to solve the simultaneous reconfiguration and optimal sizing of DGs and shunt capacitors in a distribution system.Each objective is transferred into fuzzy domain using its membership function.Then,the overall fuzzy satisfaction function is formed and considered a fitness function inasmuch as the value of this function has to be maximized to gain the optimal solution.The numerical results show that the presented algorithm improves the performance much more than other meta-heuristic algorithms.Simulation results found that simultaneous reconfiguration with DG and shunt capacitors allocation(case 5) has 77.41%,42.15%,and 56.14%improvements in power loss reduction,load balancing,and voltage profile indices,respectively in 33-bus test system.This result found 87.27%,35.82%,and 54.34%improvements of mentioned indices respectively for 69-bus system.

The Method of Fundamental Solutions for Two-Dimensional Elastostatic Problems with Stress Concentration and Highly Anisotropic Materials

The method of fundamental solutions(MFS)is a boundary-type and truly meshfree method,which is recognized as an efficient numerical tool for solving boundary value problems.The geometrical shape,boundary conditions,and applied loads can be easily modeled in the MFS.This capability makes the MFS particularly suitable for shape optimization,moving load,and inverse problems.However,it is observed that the standard MFS lead to inaccurate solutions for some elastostatic problems with stress concentration and/or highly anisotropic materials.In thiswork,by a numerical study,the important parameters,which have significant influence on the accuracy of the MFS for the analysis of two-dimensional anisotropic elastostatic problems,are investigated.The studied parameters are the degree of anisotropy of the problem,the ratio of the number of collocation points to the number of source points,and the distance between main and pseudo boundaries.It is observed that as the anisotropy of the material increases,there will be more errors in the results.It is also observed that for simple problems,increasing the distance between main and pseudo boundaries enhances the accuracy of the results;however,it is not the case for complicated problems.Moreover,it is concluded that more collocation points than source points can significantly improve the accuracy of the results.