Yujiao Zeng and Yanguang Sun
Reactive power dispatch, enhanced multi-objective particle swarmoptimization, dynamic crowding distance, mutation, chaotic de-scending inertia weight, time variant acceleration coefficients
This paper proposed a novel enhanced multi-objective particle swarm optimization (EMOPSO) for solving the optimal reactive power dispatch (ORPD) problem, which minimizes transmission loss while enhancing voltage stability. In the proposed approach, chaotic descending inertia weight and time variant acceleration coefficients can increase the algorithm’s global search capability. Mutation operator can enhance the diversity of population, and dynamic crowding distance can improve distribution of non-dominated solutions. In addition, an effective constraint handling technique is employed for considering the constraints. The effectiveness of the proposed algorithm for solving the ORPD problem is validated on the standard IEEE 30-bus and IEEE 118-bus systems under nominal and contingency states. The results are compared with those obtained by MOPSO and non-dominated sorting genetic algorithm (NSGA-II) with respect to convergence, diversity, and computational time. The numerical results demonstrate the superiority of the proposed EMOPSO in solving the ORPD problem while strictly satisfying all the constraints.
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