A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems

Abstract

In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO.

Implementation of Static Line Voltage Stability Indices for Improved Static Voltage Stability Margin

“…e optimal power ow (OPF) aims to identify the optimal solution for an objective function that is subject to several equality and inequality constraints, such as the power ow constraints, system operating limit constraints, and limits on equipment. e general OPF formulations are determined as follows [37]:…”

“…ere are three types of constraints of the OPF problem worth considering. Equations (11) to (20) describe all system constraints [37].…”

“…e optimization algorithm of this study is a hybrid DA-PSO optimization algorithm. is hybrid algorithm was proposed by Khunkitti et al [37]. It is the combination of the exploration phase of the dragonfly algorithm (DA) and exploitation phases of particle swarm optimization (PSO) algorithm.…”

“…Finally, in the exploitation phase, the PSO algorithm operates by using the global best position from DA, and the expected optimal solution can be obtained. To overcome this technique, the velocity and position equations of PSO should be modified as follows [37]:…”

Computationally efficient methods for solving optimal power flow problems by exploiting supplementary information

“…where P Gi and f i are the output power and the generation fuel cost of i -th generator, respectively, and c i0 , c i1 , and c i2 are the cost coefficients of the i-th generator which can be found in Appendix A, [52] 802.2100 N/A N/A DA-PSO [85] 802.1241 N/A N/A MTLBO [106] 801.8925 801.95 N/A PSO [52] 801.8900 N/A N/A PSOGSA [87] 801.4986 N/A N/A GWO [88] 801.2590 802.6630 804.8980 Enhanced DE [89] 801.2300 N/A N/A ABC [90] 800.6600 800.8715 801.8674 Jaya [91] 800.4986 N/A N/A DGWO [88] 800.4330 800.4674 800.4989 EADDE [8] 800.2041 N/A N/A EEA [53] 800.0831 800.1730 800.2123 EGA-DQLF [92] 799.5600 N/A N/A LTLBO [52] 799…”