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Multi-objective evolutionary algorithm based Reactive Power Dispatch with thyristor controlled series compensator

机译:晶闸管控制串联补偿器的无功调度多目标进化算法

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The Reactive Power Dispatch (RPD) problem is a nonlinear constrained optimization problem. This paper presents a multi-objective solution set for RPD problem. The objective functions are minimization of real power loss and minimization of control variable adjustment costs. This paper has considered the setting of Flexible AC Transmission System (FACTS) device as additional control parameter in the RPD formulation. In this paper, a Modified Non-Dominated Sorting Genetic Algorithm version II (MNSGA-II) is proposed for solving RPD problem. For maintaining good diversity in the performance of NSGA-II, the concepts of Dynamic Crowding Distance (DCD) is implemented in NSGA-II algorithm and given name as MNSGA-II. The standard IEEE 30-bus test system is used. The results obtained by MNSGA-II are compared with NSGA-II and validated with conventional weighted sum method using Real-coded Genetic Algorithm (RGA). The performance of NSGA-II and MNSGA-II is compared with various multi-objective performance measures namely gamma, spread, minimum spacing and Inverted Generational Distance (IGD) metrics with respect to reference pareto-front and the results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPD problem.
机译:无功功率分配(RPD)问题是非线性约束的优化问题。本文提出了RPD问题的多目标解决方案集。目标函数是最小化实际功率损耗和最小化控制变量调整成本。本文在RPD公式中考虑了将柔性交流输电系统(FACTS)设备的设置作为附加控制参数。本文提出了一种改进的非支配排序遗传算法版本II(MNSGA-II)来解决RPD问题。为了在NSGA-II的性能上保持良好的多样性,在NSGA-II算法中实现了动态拥挤距离(DCD)的概念,并将其命名为MNSGA-II。使用标准的IEEE 30总线测试系统。将MNSGA-II获得的结果与NSGA-II进行比较,并使用常规编码和算法使用实数编码遗传算法(RGA)对其进行验证。将NSGA-II和MNSGA-II的性能与相对于参考pareto-front的各种多目标性能度量(即gamma,扩展,最小间距和反向世代距离(IGD)度量)进行了比较,结果表明MNSGA-并确认其解决多目标RPD问题的潜力。

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