Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints - Chen, Gonggui; Liu, Lilan; Zhang, Zhizhong; Huang, Shanwai (Applied soft computing, 2017)
[Display omitted] •The novel constraints-handling method is proposed, which is named conditional selection strategies (CSS).•The improved GSA-based algorithms including GSA-CSS and IGSA-CSS are proposed.•Three test systems with different sizes are used to test the proposed algorithms.•According to simulation results, IGSA-CSS outperforms basic GSA, PSO and GSA-CSS; and GSA-CSS also outperforms GSA. Optimal reactive power dispatch (ORPD) is well known as a complex mixed integer nonlinear optimization problem where many constraints are required to handle. In the last decades, many artificial intelligence-based optimization methods have been used to solve ORPD problem. But, these optimization methods lack an effective means to handle constraints on state variables. Thus, in this paper, the novel and feasible conditional selection strategies (CSS) are devised to handle constraints efficiently in the proposed improved gravitational search algorithm (GSA-CSS). In addition, considering the weakness of GSA itself, the improved GSA-CSS (IGSA-CSS) is presented which employs the memory property of particle swarm optimization (PSO) to enhance global searching ability and utilizes the concept of opposition-based learning (OBL) for optimizing initial population. The presented GSA-CSS and IGSA-CSS methods are applied to ORPD problem on IEEE14-bus, IEEE30-bus and IEEE57-bus test systems for minimization of power transmission losses (Ploss) and voltage deviation (Vd), respectively. The comparisons of simulation results reveal that IGSA-CSS provides better results and the improvements of algorithm in this work are feasible and effective.