Solving optimal power flow problems using bacterial swarm optimization algorithm

The chaos particle swarm optimization algorithm was presented to solving optimal power flow. The proposed OPF considers the total cost of generators as the objective functions. To enhance the performance of algorithm, a premature convergence strategy was proposed. The strategy can be divided into two parts. In the first part, a method is introduced to judge premature convergence, while another part provides an advance method to improve the performance of algorithm with searching the solution in total feasible region. The control strategy used to prevent premature convergence will obtain starting values for initial particle before program iterating, so it can provide bitter probability of detecting global optimum solution. The simulation results on standard IEEE 30-bus system minimizing fuel cost of generator show the effectiveness of the chaos particle swarm optimization algorithm, and can obtain a good solution.

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A modified particle swarm optimization algorithm and its application in optimal power flow problem

2005 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2005.1527435 ◽

2005 ◽

Cited By ~ 6

Author(s):

Cui-Ru Wang ◽

He-Jin Yuan ◽

Zhi-Qiang Huang ◽

Jiang-Wei Zhang ◽

Chen-Jun Sun

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Power Flow ◽

Optimal Power Flow ◽

Particle Swarm Optimization Algorithm ◽

Flow Problem ◽

Swarm Optimization ◽

Modified Particle Swarm Optimization ◽

Optimal Power ◽

Power Flow Problem

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

Energies ◽

10.3390/en11092270 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2270 ◽

Cited By ~ 13

Author(s):

Sirote Khunkitti ◽

Apirat Siritaratiwat ◽

Suttichai Premrudeepreechacharn ◽

Rongrit Chatthaworn ◽

Neville Watson

Keyword(s):

Power System ◽

Optimization Algorithm ◽

Hybrid Algorithm ◽

Power Flow ◽

Optimal Power Flow ◽

Transmission Losses ◽

Dragonfly Algorithm ◽

Flow Problems ◽

Hybrid Optimization Algorithm ◽

Optimal Power

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.

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