A hybrid DA-PSO optimization algorithm for multiobjective optimal power flow problems

• Main Authors: Sirote Khunkitti, Apirat Siritaratiwat, Suttichai Premrudeepreechacharn, Rongrit Chatthaworn, Neville R. Watson
• Format: Journal
• Published: 2018
• Subjects: Energy; Engineering; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85053891915&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/62697
• Institution: Chiang Mai University

© 2018 by the authors. 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.